Dissertations / Theses: 'User Personalization'

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Vieira, André Fonseca dos Santos Dias. "Context-aware personalization environment for mobile computing." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8649.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática Currently, we live in a world where the amount of on-line information vastly outstrips any individual’s capability to survey it. Filtering that information in order to obtain only useful and interesting information is a solution to this problem. The mobile computing area proposes to integrate computation in users’ daily activities in an unobtrusive way, in order to guarantee an improvement in their experience and quality of life. Furthermore, it is crucial to develop smaller and more intelligent devices to achieve this area’s goals, such as mobility and energy savings. This computing area reinforces the necessity to filter information towards personalization due to its humancentred paradigm. In order to attend to this personalization necessity, it is desired to have a solution that is able to learn the users preferences and needs, resulting in the generation of profiles that represent each style of interaction between a user and an application’s resources(e.g. buttons and menus). Those profiles can be obtained by using machine learning algorithms that use data derived from the user interaction with the application, combined with context data and explicit user preferences. This work proposes an environment with a generic context-aware personalization model and a machine learning module. It is provided the possibility to personalize an application, based on user profiles obtained from data, collected from implicit and explicit user interaction. Using a provided personalization API (Application Programming Interface) and other configuration modules, the environment was tested on LEY (Less energy Empowers You), a persuasive mobile-based serious game to help people understand domestic energy usage.

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SONG, Songbo. "Advanced personalization of IPTV services." Phd thesis, Institut National des Télécommunications, 2012. http://tel.archives-ouvertes.fr/tel-00814620.

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Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users' experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users' devices 'screen types, size, supported resolution, '' and networks available network types to be used by the user, available bandwidth, ..') as well as the context of the service itself (content type and description, available format 'HD/SD', available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users' identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user's environment (devices and networks status), interpreting user's requirements and making the user's interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users' preferences) which in turn highly improves the users' Quality of Experience (QoE) and enriching the offers of IPTV services

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Song, Songbo. "Advanced personalization of IPTV services." Thesis, Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001/document.

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Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d’une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d’autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type ‘nTS’ (pour ‘network Time Shifting’ en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type ‘nPVR’ (pour ‘network Personal Video Recorder’ en anglais) qui permettent d’enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l’utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l’utilisateur. L’adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n’est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l’appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l’utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l’utilisateur et aussi des informations contextes Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users’ experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users’ devices ‘screen types, size, supported resolution, ‘‘ and networks available network types to be used by the user, available bandwidth, ..’) as well as the context of the service itself (content type and description, available format ‘HD/SD’, available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users’ identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user’s environment (devices and networks status), interpreting user’s requirements and making the user’s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users’ preferences) which in turn highly improves the users’ Quality of Experience (QoE) and enriching the offers of IPTV services

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Rawat, Rakesh. "User behaviour modelling in a multi-dimensional environment for personalization and recommendation." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/48135/1/Rakesh_Rawat_Thesis.pdf.

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Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.

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Song, Songbo. "Advanced personalization of IPTV services." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2012. http://www.theses.fr/2012TELE0001.

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Le monde de la TV est en cours de transformation de la télévision analogique à la télévision numérique, qui est capable de diffuser du contenu de haute qualité, offrir aux consommateurs davantage de choix, et rendre l'expérience de visualisation plus interactive. IPTV (Internet Protocol TV) présente une révolution dans la télévision numérique dans lequel les services de télévision numérique sont fournis aux utilisateurs en utilisant le protocole Internet (IP) au dessus d’une connexion haut débit. Les progrès de la technologie IPTV permettra donc un nouveau modèle de fourniture de services. Les fonctions offertes aux utilisateurs leur permettent de plus en plus d’autonomie et de plus en plus de choix. Il en est notamment ainsi de services de type ‘nTS’ (pour ‘network Time Shifting’ en anglais) qui permettent à un utilisateur de visionner un programme de télévision en décalage par rapport à sa programmation de diffusion, ou encore des services de type ‘nPVR’ (pour ‘network Personal Video Recorder’ en anglais) qui permettent d’enregistrer au niveau du réseau un contenu numérique pour un utilisateur. D'autre part, l'architecture IMS proposée dans NGN fournit une architecture commune pour les services IPTV. Malgré les progrès rapides de la technologie de télévision interactive (comprenant notamment les technologies IPTV et NGN), la personnalisation de services IPTV en est encore à ses débuts. De nos jours, la personnalisation des services IPTV se limite principalement à la recommandation de contenus et à la publicité ciblée. Ces services ne sont donc pas complètement centrés sur l’utilisateur, alors que choisir manuellement les canaux de diffusion et les publicités désirées peut représenter une gêne pour l’utilisateur. L’adaptation des contenus numériques en fonction de la capacité des réseaux et des dispositifs utilisés n’est pas encore prise en compte dans les implémentations actuelles. Avec le développement des technologies numériques, les utilisateurs sont amenés à regarder la télévision non seulement sur des postes de télévision, mais également sur des smart phones, des tablettes digitales, ou encore des PCs. En conséquence, personnaliser les contenus IPTV en fonction de l’appareil utilisé pour regarder la télévision, en fonction des capacités du réseau et du contexte de l’utilisateur représente un défi important. Cette thèse présente des solutions visant à améliorer la personnalisation de services IPTV à partir de trois aspects: 1) Nouvelle identification et authentification pour services IPTV. 2) Nouvelle architecture IPTV intégrée et comportant un système de sensibilité au contexte pour le service de personnalisation. 3) Nouveau service de recommandation de contenu en fonction des préférences de l’utilisateur et aussi des informations contextes Internet Protocol TV (IPTV) delivers television content to users over IP-based network. Different from the traditional TV services, IPTV platforms provide users with large amount of multimedia contents with interactive and personalized services, including the targeted advertisement, on-demand content, personal video recorder, and so on. IPTV is promising since it allows to satisfy users experience and presents advanced entertainment services. On the other hand, the Next Generation Network (NGN) approach in allowing services convergence (through for instance coupling IPTV with the IP Multimedia Subsystem (IMS) architecture or NGN Non-IMS architecture) enhances users’ experience and allows for more services personalization. Although the rapid advancement in interactive TV technology (including IPTV and NGN technologies), services personalization is still in its infancy, lacking the real distinguish of each user in a unique manner, the consideration of the context of the user (who is this user, what is his preferences, his regional area, location, ..) and his environment (characteristics of the users’ devices ‘screen types, size, supported resolution, ‘‘ and networks available network types to be used by the user, available bandwidth, ..’) as well as the context of the service itself (content type and description, available format ‘HD/SD’, available language, ..) in order to provide the adequate personalized content for each user. This advanced IPTV services allows services providers to promote new services and open new business opportunities and allows network operators to make better utilization of network resources through adapting the delivered content according to the available bandwidth and to better meet the QoE (Quality of Experience) of clients. This thesis focuses on enhanced personalization for IPTV services following a user-centric context-aware approach through providing solutions for: i) Users’ identification during IPTV service access through a unique and fine-grained manner (different from the identification of the subscription which is the usual current case) based on employing a personal identifier for each user which is a part of the user context information. ii) Context-Aware IPTV service through proposing a context-aware system on top of the IPTV architecture for gathering in a dynamic and real-time manner the different context information related to the user, devices, network and service. The context information is gathered throughout the whole IPTV delivery chain considering the user domain, network provider domain, and service/content provider domain. The proposed context-aware system allows monitoring user’s environment (devices and networks status), interpreting user’s requirements and making the user’s interaction with the TV system dynamic and transparent. iii) Personalized recommendation and selection of IPTV content based on the different context information gathered and the personalization decision taken by the context-aware system (different from the current recommendation approach mainly based on matching content to users’ preferences) which in turn highly improves the users’ Quality of Experience (QoE) and enriching the offers of IPTV services

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Sadasivam, Rajani Shankar. "An architecture framework for composite services with process-personalization." Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2009r/sadasivam.pdf.

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Thesis (Ph. D.)--University of Alabama at Birmingham, 2007. Title from PDF title page (viewed Feb. 4, 2010). Additional advisors: Barrett R. Bryant, Chittoor V. Ramamoorthy, Jeffrey H. Kulick, Gary J. Grimes, Gregg L. Vaughn, Murat N. Tanju. Includes bibliographical references (p. 161-183).

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Mitthalal, Dipesh Dugar. "Social Proximity Indicator Application Enhancing Personalization for the User : A User Centric Multimodal Smartphone based Social Proximity Indicator." Thesis, KTH, Radio Systems Laboratory (RS Lab), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-147677.

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The smartphone has become an important part of our daily life. It takes advantage of multiple built-in sensors, along with internet connectivity, to provide a variety of services including navigation, weather forecasts, media capturing/sharing, and many more. The smartphone has become a hub for our social interactions, ranging from traditional voice communications through messaging, to online social networking. There are endless possibilities for a new generation of applications that automatically adapts according to users’ social affinities. The key enabler is to understand the social profile of the user and to realize a Social Proximity Indicator (SPI). This indicator of a user’s social profile includes the user’s location, preferences, common friends, frequent interactions among friends, community membership, and many more attributes. This project addresses the exploitation of the user’s smartphone as a detector of these user’s social contexts in order to infer the social proximity between any 2 (or more) people. The goal of this social proximity indicator is to identify the (current) set of smartphone users that would want to participate in an application specific activity, such as media sharing, group conversation, etc. SPI identifies users based on their social interactions and common preferences. Based on this SPI, a photo sharing application is proposed and built for the Android smartphone platform. This application uses multiple physical and virtual sensors (hosted by the user’s smartphone) to determine the context of the user. After inferring this context, the application initiates photo sharing among an identified group sharing this context. The application, as well as the underlying code, theoretical concepts, and results are presented in this thesis. The experiments conducted during this thesis project indicate that it is feasible to build smartphone based social proximity based applications in which proximity incorporates much more than simply physical location, thus going beyond existing applications. Smartphone har blivit en viktig del av vårt dagliga liv, som drar nytta av de många inbyggda sensorer, inklusive Internet-anslutning, för att ge en mängd olika tjänster. Däribland navigation, väderprognoser, media fånga/delning, och många fler tjänster. Smartphone har blivit kärnan i våra sociala interaktioner. Den erbjuder allt från traditionella röst kommunikation via meddelanden, till sociala nätverk. Det finns oändliga möjligheter för nästa generationens program som kommer att automatiskt anpassar sig till användarnas sociala tillhörighet. Den viktigaste faktorn är att förstå den sociala profilen av användaren genom att använda ett Socialt Proximity Indikator (SPI). Denna indikator på social profil innehåller användarens läge, inställningar, gemensamma vänner, täta samspel mellan vänner, gemenskap medlemskap, och många fler attribut . Detta projekt behandlar utnyttjandet av användarens smartphone som en detektor av användarens sociala sammanhang för att ansluta sig till social närhet mellan några två (eller fler) personer. Målet med denna indikator är att identifiera den (nuvarande) uppsättning av smartphone-användare som skulle vilja delta i någon applikation specifik aktivitet, till exempel mediedelning, gruppsamtal, etc. SPI identifiera användare baserat på deras sociala interaktioner och gemensamma preferenser. En fotodelnings applikation har föreslagits och byggd för Android smartphone -plattformen, baserad på data från SPI. Denna applikation använder flera fysiska och virtuella sensorer (genom användarens smartphone) för att bestämma ramen för användaren. Efter en analys kommer programmet att initiera fotodelning mellan den identifierade gruppen som hittades i analysen. Applikationen, liksom den underliggande koden ,teoretiska begrepp, och resultaten kommer att presenteras i denna uppsats. Experimenten som genomfördes under detta examensarbete tyder på att det är möjligt att bygga smartphone baserad på SPI där närhet innehåller mycket mer än bara fysisk plats. Det gör applikationen unik än de befintliga applikationer.

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Fan, Haiyan. "Web personalization - a typology, instrument, and a test of a predictive model." Thesis, [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1581.

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Krause, Daniel [Verfasser]. "A semantic web service-based framework for generic personalization and user modeling / Daniel Krause." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2012. http://d-nb.info/1024679721/34.

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Li, Lei. "Next Generation of Recommender Systems: Algorithms and Applications." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1446.

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Personalized recommender systems aim to assist users in retrieving and accessing interesting items by automatically acquiring user preferences from the historical data and matching items with the preferences. In the last decade, recommendation services have gained great attention due to the problem of information overload. However, despite recent advances of personalization techniques, several critical issues in modern recommender systems have not been well studied. These issues include: (1) understanding the accessing patterns of users (i.e., how to effectively model users' accessing behaviors); (2) understanding the relations between users and other objects (i.e., how to comprehensively assess the complex correlations between users and entities in recommender systems); and (3) understanding the interest change of users (i.e., how to adaptively capture users' preference drift over time). To meet the needs of users in modern recommender systems, it is imperative to provide solutions to address the aforementioned issues and apply the solutions to real-world applications. The major goal of this dissertation is to provide integrated recommendation approaches to tackle the challenges of the current generation of recommender systems. In particular, three user-oriented aspects of recommendation techniques were studied, including understanding accessing patterns, understanding complex relations and understanding temporal dynamics. To this end, we made three research contributions. First, we presented various personalized user profiling algorithms to capture click behaviors of users from both coarse- and fine-grained granularities; second, we proposed graph-based recommendation models to describe the complex correlations in a recommender system; third, we studied temporal recommendation approaches in order to capture the preference changes of users, by considering both long-term and short-term user profiles. In addition, a versatile recommendation framework was proposed, in which the proposed recommendation techniques were seamlessly integrated. Different evaluation criteria were implemented in this framework for evaluating recommendation techniques in real-world recommendation applications. In summary, the frequent changes of user interests and item repository lead to a series of user-centric challenges that are not well addressed in the current generation of recommender systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective recommendation framework.

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Abdullah, Noraswaliza. "Integrating collaborative filtering and matching-based search for product recommendation." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/59507/1/Noraswaliza_Abdullah_Thesis.pdf.

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Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and thus help them in making good decisions about which product to buy from the vast number of product choices available to them. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based recommender system approaches. These approaches are not suitable for recommending luxurious and infrequently purchased products as they rely on a large amount of ratings data that is not usually available for such products. This research aims to explore novel approaches for recommending infrequently purchased products by exploiting user generated content such as user reviews and product click streams data. From reviews on products given by the previous users, association rules between product attributes are extracted using an association rule mining technique. Furthermore, from product click streams data, user profiles are generated using the proposed user profiling approach. Two recommendation approaches are proposed based on the knowledge extracted from these resources. The first approach is developed by formulating a new query from the initial query given by the target user, by expanding the query with the suitable association rules. In the second approach, a collaborative-filtering recommender system and search-based approaches are integrated within a hybrid system. In this hybrid system, user profiles are used to find the target user’s neighbour and the subsequent products viewed by them are then used to search for other relevant products. Experiments have been conducted on a real world dataset collected from one of the online car sale companies in Australia to evaluate the effectiveness of the proposed recommendation approaches. The experiment results show that user profiles generated from user click stream data and association rules generated from user reviews can improve recommendation accuracy. In addition, the experiment results also prove that the proposed query expansion and the hybrid collaborative filtering and search-based approaches perform better than the baseline approaches. Integrating the collaborative-filtering and search-based approaches has been challenging as this strategy has not been widely explored so far especially for recommending infrequently purchased products. Therefore, this research will provide a theoretical contribution to the recommender system field as a new technique of combining collaborative-filtering and search-based approaches will be developed. This research also contributes to a development of a new query expansion technique for infrequently purchased products recommendation. This research will also provide a practical contribution to the development of a prototype system for recommending cars.

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Shao, Hong, and Aida Amirfallah. "Providing Resources to Target User Groups through Customization of Web Site." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH. Forskningsmiljö Informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-17711.

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In this thesis, we plan to use a group-based semantic-expansion approach to design a new personalised system framework. Semantic web and group preference offer solution to the above problem. In this thesis, ontologies and semantic techniques are applied in different components of the framework. Information has been gathered from different resources and each of the resource might be using various types of identifiers for the same concept, therefore semantic web technologies are used to find out if the concept is the same or not. On the other hand, we create group preference in our personalization system. If the system fails to obtain personal preference from new user, group preference supports the system providing recommendation to the new user according to group classification.

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YOUNUS, ARJUMAND. "Utilizing Social Breadcrumbs for User Profiling in Personalized Applications." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/96209.

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The rapid growth of World Wide Web and huge amount of information on it gave birth to “information overload”; an effective way to deal with this problem is through personalized applications such as personalized Web search engines and recommendation systems. An important aspect of any personalization effort however is a model that can serve as an accurate representation for the user. This model plays a central role as it serves as the engine based on which personalized content will be served to a specific user. Given the widespread activities of users on social networking platforms, these platforms can provide useful indications relating to a user’s interests and preferences. Traditional sources for user modelling are controversial in nature in line with the loss of privacy associated with them. More recent sources such as bookmarks and social tags have proven to be less effective; we therefore propose microblog acitivity of users as an alternate source to the aim of creating effective user profiles. Moreover, microblog activity of those to whom a user is connected is utilized in our model. Experimental evaluations in two application scenarios namely personalized Web search and scientific articles’ recommendation demonstrates the superiority of our model.

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Speirs, James C. "Website Adaptive Navigation Effects on User Experiences." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3536.

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The information search process within a website can often be frustrating and confusing for website visitors. Navigational structures are often complex and multitiered, hiding links with several layers of navigation that user's might be interested in. Poor navigation causes user frustration. Adaptive navigation can be used to improve the user's navigational experience by flattening the navigational structure and reducing the number of accessible links to only those that the user would be interested in. This examines the effects on a user's navigational experience, of using adaptive navigation as the main navigational structure on a website. This study measured these effects by gathering survey responses from over 1,000 users. The survey recorded users' perceptions of navigational effectiveness and efficiency as well as user satisfaction and efficacy. Users were assigned into nine treatment groups that provided variations in navigational change frequency and the order of navigational links. Surveys were used to identify the effects of navigational change frequency and navigational link ordering on the user's navigational experience. The survey found that adaptive navigation works best when change occurs on a page-by-page basis and links are ordered alphabetically.

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Ericsson, Tomas, and Monika Nilqvist. "A Personalized Car : A study on how to apply personalization to a driver environment." Thesis, Linköping University, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7795.

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An increasing amount of technology in cars makes new ideas and solutions necessary. This study will explore the idea of a personalized driver environment and investigate possible benefits and drawbacks with such a feature. The study consists of three parts: a pre-study exploring personalization, a survey investigating the attitudes towards personal settings, and finally an interview testing a specific solution. The survey was distributed in USA and Sweden while the interviews were conducted with Swedish subjects.Overall, the concept of a personalized car has been well received. This study has shown that the most requested settings are associated with the driver position, hi-fi system and climate. The study also suggests that feeling in control of the personalization is more important than the benefits associated with automation. The user prefers visible solutions, such as a personal button on the key before hidden (e.g. using a button sequence or a menu system). Such a button promotes the feature while allowing the user to interact with the car in a familiar way. However, since little real user experience exists with such solutions it is important to continue research when further developing personalization of a car.

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Pahnila, S. (Seppo). "Assessing the usage of personalized web information systems." Doctoral thesis, University of Oulu, 2006. http://urn.fi/urn:isbn:9514281128.

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Abstract The focus of this thesis is to assess factors which have an influence on the acceptance of personalized Web information systems (WIS). This study is primarily based on the widely used and accepted Technology Acceptance Model (TAM). The qualitative part of the study presents an approach for developing dynamic Web information systems. The development process was based on the work done in the OWLA (Open Web/Wireless Adaptive Hypermedia Applications) research project in 2000–2002. The quantitative part of the study, which includes the main objective of the study, is related to assessment of user acceptance of the given system. The researcher performed a field study in 2003 in which data was collected using a web questionnaire. The field study resulted in a sample size of 209 responses. Moreover we carried out a comparison with the 2002/2003 field study aiming to study possible differences in respondents' attitude toward personalization. The previous field study was carried out during the OWLA research project. The responses were analyzed using SPSS and AMOS. Results of the quantitative part of the study show that the respondents accept personalization but they like to and want to adapt and personalize the content themselves. This was one of the most important findings of the study. Respondents do not want the system to provide content automatically; they want to control the content themselves. The results of the 2003 field study indicate that respondents do not consider layout adaptation, whether automatic or manual, to be important or desirable. The formulated model reveals that perceived relevance is one of the most important aspects that have an effect on attitude toward using and intention to use the given IS. Perceived expectancy and intention to use the given WIS are significant antecedents of actual use. Perceived behavioral control and normative beliefs also have a significant effect on intention to use portal. Enjoyment has a significant effect on attitude. Overall, we have identified in this study some significant factors which will provide useful information to IS practitioners studying the voluntary adoption of specific personalized systems. The results will also provide useful information for systems designers and will contribute towards assessing possible individual barriers to the use of personalized information systems. The study includes several proposals for future research including developing deeper understanding activities related to presented development approach, creating a more detailed view of factors which have an influence on the user's decision to use or not to use personalized information system, studying several systems.

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Wang, Ying, and 王莹. "The effects of website personalization on user intention to return through cognitive beliefs and affective reactions." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B41757944.

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Wang, Ying. "The effects of website personalization on user intention to return through cognitive beliefs and affective reactions." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B41757944.

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Lal, Neeraj. "Enhancing Location-Based Content Delivery Through Semi-Automated Generation of User Profile." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1293731843.

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Kirmemis, Oznur. "Openmore: A Content-based Movie Recommendation System." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609479/index.pdf.

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The tremendous growth of Web has made information overload problem increasingly serious. Users are often confused by huge amount of information available on the internet and they are faced with the problem of finding the most relevant information that meets their needs. Recommender systems have proven to be an important solution approach to this problem. This thesis will present OPENMORE, a movie recommendation system, which is primarily based on content-based filtering technique. The distinctive point of this study lies in the methodology used to construct and update user and item profiles and the optimizations used to fine-tune the constructed user models. The proposed system arranges movie content data as features of a set of dimension slots, where each feature is assigned a stable feature weight regardless of individual movies. These feature weights and the explicit feedbacks provided by the user are then used to construct the user profile, which is fine-tuned through a set of optimization mechanisms. Users are enabled to view their profile, update them and create multiple contexts where they can provide negative and positive feedback for the movies on the feature level.

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Khoury, Vanessa El. "Semantic protection and personalization of video content PIAF : MPEG compliant adaptation framework preserving the user perceived quality." Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0086/document.

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Dans cette thèse, nous proposons un framework d’adaptation appelé "Personalized vIdeo Adaptation Framework" (PIAF) conçu à partir des standards MPEG. PIAF intègre les contraintes sémantiques et vise à maximiser la qualité perçue par l’utilisateur lors de la visualisation de la vidéo tout en respectant les droits de propriété intellectuelle. Les contributions de cette thèse peuvent être résumées comme suit. Dans un premier temps, nous avons utilisé et étendu les standards MPEG-7 et MPEG-21 afin de représenter les préférences des utilisateurs. Nous avons ensuite proposé un modèle formel du processus d’adaptation sémantique d’une vidéo et défini une fonction d’utilité régissant le mécanisme de prise de décision du MPDA. Cette fonction tient compte de différentes dimensions de qualité (qualité perceptuelle, sémantique, temps d’exécution nécessaire) afin d’évaluer quantitativement la qualité d’un plan d’adaptation. Le processus d’adaptation que nous proposons intègre les droits de propriété intellectuelle dans le processus de décision. Dans certains cas, le plan d’adaptation qui produirait la vidéo de meilleure qualité adaptée aux préférences de l’utilisateur peut être inapplicable car il ne respecte pas les contraintes du propriétaire. Trouver le meilleur plan d’adaptation devient alors un problème NP-complet. Nous avons proposé une solution pratique à ce problème sous la forme d’une heuristique capable de sélectionner un plan très proche de l’optimum en un temps de calcul raisonnable. Afin d’implémenter ce framework, nous avons également développé un outil d’annotation sémantique de contenu vidéo (SVCAT) qui produit des annotations sémantiques structurelles et de haut niveau selon un modèle objet basé sur du contenu vidéo. Nous avons validé nos travaux avec des évaluations qualitatives et quantitatives qui nous ont permis d’étudier la performance et l'efficacité du MPDA. Nous avons validé nos travaux avec des évaluations qualitatives et quantitatives qui nous ont permis d’étudier la performance et l'efficacité du MPDA. Les résultats obtenus démontrent que la fonction d’utilité proposée présente une forte corrélation avec les évaluations subjectives fournies par des utilisateurs concernant la qualité d’une vidéo adaptée, et constitue donc une base tout à fait pertinente pour le MPDA Universal Multimedia Experience (UME) is the notion that a user should receive informative adapted content anytime and anywhere. Personalization of videos, which adapts their content according to user preferences, is a vital aspect of achieving the UME vision. User preferences can be translated into several types of constraints that must be considered by the adaptation process, including semantic constraints directly related to the content of the video. The overall goal of this adaptation process is to provide users with adapted content that maximizes their Quality of Experience (QoE). This QoE depends at the same time on the level of the user's satisfaction in perceiving the adapted content, the amount of knowledge assimilated by them, and the adaptation execution time. In video adaptation frameworks, the Adaptation Decision Taking Engine (ADTE), is responsible for achieving this goal. The task of the ADTE is challenging as many adaptation operations can satisfy the same semantic constraint, and thus arising in several feasible adaptation plans. Indeed, for each entity to be adapted, the ADTE must decide on the adequate adaptation operator that satisfies the user's preferences while maximizing his/her quality of experience. The first challenge to achieve in this is to objectively measure the quality of the adapted video, while considering the multiple aspects of the QoE. The second challenge is to assess beforehand this quality in order to choose the most appropriate adaptation plan among all possible ones. The third challenge is to resolve conflicting or overlapping semantic constraints, in particular conflicts arising from constraints expressed by owner's intellectual property rights (IPR) about the modification of the content. In this thesis, we tackled the aforementioned challenges by proposing a Utility Function (UF), which integrates semantic concerns with user's perceptual considerations. This UF models the relationships among adaptation operations, user preferences, and the quality of the video content. We integrated this UF into an ADTE. This ADTE performs a multi-level piecewise reasoning to choose the adaptation plan that maximizes the user-perceived quality. Furthermore, we included IPR in the adaptation process. Thereby, we modeled content owner constraints, and proposed a heuristic to resolve conflicting user and owner constraints. More, we developed SVCAT, which produces structural and high-level semantic annotation according to an original object-based video content model. We modeled as well the user's preferences proposing extensions to MPEG-7 and MPEG-21. All the developed contributions were carried out as part of a coherent framework called PIAF. We validated this research with qualitative and quantitative evaluations, which assess the performance and the efficiency of the proposed adaptation decision-taking engine within PIAF

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Bäckerås, Johannes. "What do skin care consumers think about personalization of content, user interface or a combination of both?" Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18880.

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Background The cosmetic industry is growing at a fast pace and, like other industries, there are a few problems regarding e-retailing that makes it hard for customers to make optimal purchase decision. Example of problems are; the number of e-retailers increase, e-retailers’ variety of products increase, and the content is almost unlimited. Companies have started to personalize to solve these problems. However, there is a lack in the literature regarding how-to best design personalization with regard to its dimensions and subcategories. Therefore, the next step is to get an understanding of which dimensions and subcategories that are the most effective. Objectives At present, the majority of the literature regarding personalization focus on how personalization can be performed and not in which way personalization should be performed to increase customer satisfaction and customer loyalty. This means that the knowledge regarding the subcategories within personalization and their effectiveness is limited. Therefore, the purpose of this thesis is to increase the body of knowledge regarding which subcategories that is the most effective in increasing customer satisfaction and/or loyalty. Knowledge about this topic can give companies perspectives to know how personalization should be designed to increase customer satisfaction and customer loyalty. Methods This study was a survey type, resulting in primary quantitative data collecting and analyzes. A pilot test was first made, before the final questionnaire. This to test the reliability and validity. The data from the pilot study were partly analyzed in SPSS and partly analyzed manually. The data from the final questionnaire were then analyzed in SPSS. Results Regarding customer satisfaction, there is a statistically significant difference between content personalization and AIGUI (Adaptive Interface and Graphical User Interface) personalization as well as between AIGUI personalization and combination (content and AIGUI) personalization. However, there is no statistical difference between content personalization and combination personalization. Regarding customer loyalty, the is no statistical difference between the personalization groups. Conclusions An e-retailer in the cosmetic industry should focus on content personalization or combination personalization Bakgrund Industrin om kosmetik växer i en snabb takt och likt andra industrier finns det problem, kring återförsäljare som använder sig utav e-handel, som gör det svårt för kunderna att göra optimala köpbeslut. Exempel på problem är; Antalet återförsäljare som använder sig utav e-handel ökar, deras variationer av produkter ökar och informationen är i princip oändlig. Mål Litteraturen kring personligfiering handlar till större delen om hur personligfiering kan utformas och inte på vilket sätt personligfiering borde utformas för att öka kundnöjdhet och kundlojalitet. Detta innebär att kunskapen kring underkategorier av personligfiering och dess effektivitet är begränsad. Därför är syftet med studien att öka kunskapen kring underkategorier av personligfiering och ta reda på hur dessa påverkar kundnöjdhet och/eller kundlojalitet. Kunskap om detta kan ge företag perspektiv på hur de borde utforma sin personligfiering för att öka kundnöjdhet och kundlojalitet. Metod Denna studien använde sig utav en undersökning för att besvara avhandlingsfrågan. Detta resulterade i en studie av primär kvantitativ datainsamling och analysering. Först genomfördes en pilotstudie för att testa pålitligheten och giltigheten. Datan från pilotstudien var delvis analyserad i SPSS och delvis analyserad manuellt. Datan från den slutgiltiga käten analyserades i SPSS. Resultat Vad gäller kundnöjdhet framkom det att det finns en statistisk signifikant skillnad mellan innehålls personligfiering och AIGUI (Adaptive Interface and Graphical User Interface) personligfiering. Det finns också en statistisk signifikant skillnad mellan AIGUI personligfiering och combination (innehåll och AIGUI) personligfiering. Däremot framkom det att det inte finns någon statistisk signifikant skillnad mellan innehålls personligfiering och combination personligfiering. Vad gäller kundlojalitet framkom det att det inte finns någon statistisk signifikant skillnad mellan de olika personligfieringstyperna. Slutsatser En återförsäljare av kosmetik som använder sig utav e-handel borde fokusera på innehåll personligfiering eller combination personligfiering.

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Kim, Seonho. "Visualizing Users, User Communities, and Usage Trends in Complex Information Systems Using Implicit Rating Data." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/27286.

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Research on personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users. There it is possible to classify items involved and to personalize based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as digital libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. For this reason, more research on implicit rating data is recommended, because it is easy to obtain, suffers less from terminology issues, is more informative, and contains more user-centered information. In previous reports on my doctoral work, I discussed collecting, storing, processing, and utilizing implicit rating data of digital libraries for analysis and decision support. This dissertation presents a visualization tool, VUDM (Visual User-model Data Mining tool), utilizing implicit rating data, to demonstrate the effectiveness of implicit rating data in characterizing users, user communities, and usage trends of digital libraries. The results of user studies, performed both with typical end-users and with library experts, to test the usefulness of VUDM, support that implicit rating data is useful and can be utilized for digital library analysis software, so that both end users and experts can benefit. Ph. D.

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Canal, Camprodon Gerard. "Adapting robot behavior to user preferences in assistive scenarios." Doctoral thesis, TDX (Tesis Doctorals en Xarxa), 2020. http://hdl.handle.net/10803/669799.

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Robotic assistants have inspired numerous books and science fiction movies. In the real world, these kinds of devices are a growing need in amongst the elderly, who while life continue requiring more assistance. While life expectancy is increasing, life quality is not necessarily doing so. Thus, we may find ourselves and our loved ones being dependent and needing another person to perform the most basic tasks, which has a strong psychological impact. Accordingly, assistive robots may be the definitive tool to give more quality of life by empowering dependent people and extending their independent living. Assisting users to perform daily activities requires adapting to them and their needs, as they might not be able to adapt to the robot. This thesis tackles adaptation and personalization issues through user preferences. We 'focus on physical tasks that involve close contact, as these present interesting challenges, and are of great importance for he user. Therefore, three tasks are mainly used throughout the thesis: assistive feeding, shoe fitting, and jacket dressing. We first describe a framework for robot behavior adaptation that illustrates how robots should be personalized for and by end- users or their assistants. Using this framework, non-technical users determine how !he robot should behave. Then, we define the concept of preference for assistive robotics scenarios and establish a taxonomy, which includes hierarchies and groups of preferences, grounding definitions and concepts. We then show how the preferences in the taxonomy are used with Al planning systems to adapt the robot behavior to the preferences of the user obtained from simple questions. Our algorithms allow for long-term adaptations as well as to cope with misinformed user models. We further integrate the methods with low-level motion primitives that provide a more robust adaptation and behavior while lowering the number of needed actions and demonstrations. Moreover, we perform a deeper analysis in Planning and preferences with the introduction of new algorithms to provide preference suggestions in planning domains. The thesis then concludes with a user study that evaluates the use of the preferences in the three real assistive robotics scenarios. The experiments show a clear understanding of the preferences of users, who were able to assess the impact of their preferences on the behavior of the robot. In summary, we provide tools and algorithms to design the robotic assistants of the future. Assistants that should be able to adapt to the assisted user needs and preferences, just as human assistants do nowadays. Els assistents robòtics han inspirat nombrosos llibres i pel·lícules de ciència-ficció al llarg de la història. Però tornant al món real, aquest tipus de dispositius s'estan tornant una necessitat per a una societat que envelleix a un ritme ràpid i que, per tant, requerirà més i més assistència. Mentre l'esperança de vida augmenta, la qualitat de vida no necessàriament ho fa. Per tant, ens podem trobar a nosaltres mateixos i als nostres estimats en una situació de dependència, necessitant una altra persona per poder fer les tasques més bàsiques, cosa que té un gran impacte psicològic. En conseqüència, els robots assistencials poden ser l'eina definitiva per proporcionar una millor qualitat de vida empoderant els usuaris i allargant la seva capacitat de viure independentment. L'assistència a persones per realitzar tasques diàries requereix adaptar-se a elles i les seves necessitats, donat que aquests usuaris no poden adaptar-se al robot. En aquesta tesi, abordem el problema de l'adaptació i la personalització d'un robot mitjançant preferències de l'usuari. Ens centrem en tasques físiques, que involucren contacte amb la persona, per les seves dificultats i importància per a l'usuari. Per aquest motiu, la tesi utilitzarà principalment tres tasques com a exemple: donar menjar, posar una sabata i vestir una jaqueta. Comencem definint un marc (framework) per a la personalització del comportament del robot que defineix com s'han de personalitzar els robots per usuaris i pels seus assistents. Amb aquest marc, usuaris sense coneixements tècnics són capaços de definir com s'ha de comportar el robot. Posteriorment definim el concepte de preferència per a robots assistencials i establim una taxonomia que inclou jerarquies i grups de preferències, els quals fonamenten les definicions i conceptes. Després mostrem com les preferències de la taxonomia s'utilitzen amb sistemes planificadors amb IA per adaptar el comportament del robot a les preferències de l'usuari, que s'obtenen mitjançant preguntes simples. Els nostres algorismes permeten l'adaptació a llarg termini, així com fer front a models d'usuari mal inferits. Aquests mètodes són integrats amb primitives a baix nivell que proporcionen una adaptació i comportament més robusts a la mateixa vegada que disminueixen el nombre d'accions i demostracions necessàries. També fem una anàlisi més profunda de l'ús de les preferències amb planificadors amb la introducció de nous algorismes per fer suggeriments de preferències en dominis de planificació. La tesi conclou amb un estudi amb usuaris que avalua l'ús de les preferències en les tres tasques assistencials. Els experiments demostren un clar enteniment de les preferències per part dels usuaris, que van ser capaços de discernir quan les seves preferències eren utilitzades. En resum, proporcionem eines i algorismes per dissenyar els assistents robòtics del futur. Uns assistents que haurien de ser capaços d'adaptar-se a les preferències i necessitats de l'usuari que assisteixen, tal com els assistents humans fan avui en dia.

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Clay, Paul F. "Factors contributing to user choice between codification and personalization-based knowledge management systems a task-technology fit perspective /." [Bloomington, Ind.] : Indiana University, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3219901.

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Thesis (Ph.D.)--Indiana University, Kelley School of Business, 2006. Source: Dissertation Abstracts International, Volume: 67-06, Section: A, page: 2227. Adviser: Alan R. Dennis. "Title from dissertation home page (viewed June 21, 2007)."

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Li, Yu-Chang. "Digitization of Museum and Art Gallery: A Framework for Enhancing User Interactivity and Personalization in On-site Environments." Thesis, Griffith University, 2015. http://hdl.handle.net/10072/367886.

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Digital curation is an emerging topic of interest in the current museum development. If on-site museum experience can be enhanced by digital technology, the development of digital curation in cultural expression can not only promote the role of museum in this digital era but also boost the value of cultural heritage more than ever before. This research focuses on the development of digital curation in cultural expression applications, such as museums and art galleries, with special attention to the user experience perspective. The goal is to provide a novel approach that helps to enhance visitors’ on-site museum experience through digital technology. We illustrate how the use of technology can improve the production process and increase the interaction and personalization between visitors and collections by evaluating a prototype that is developed based on this principle. Thesis (PhD Doctorate) Doctor of Philosophy (PhD) School of Information and Communication Technology Science, Environment, Engineering and Technology Full Text

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Valencia, Rodríguez Salvador. "Location Aware Multi-criteria Recommender System for Intelligent Data Mining." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/23418.

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One of the most important challenges facing us today is to personalize services based on user preferences. In order to achieve this objective, the design of Recommender Systems (RSs), which are systems designed to aid the users through different decision-making processes by providing recommendations to them, have been an active area of research. RSs may produce personalized and non-personalized recommendations. Non-personalized RSs provide general suggestions to a user, based on the number of times an item has been selected in the past. Personalized RSs, on the other hand, aim to predict the most suitable items for a specific user, based on the user’s preferences and constraints. The latter are the focus of this thesis. While Recommender Systems have been successful in many domains, a number of challenges remain. For example, most implementations consider only single criteria ratings, and consequently are unable to identify why a user prefers an item over others. Many systems classify the user into one single group or cluster which is an unrealistic approach, since in real world users share commonalities in different degrees with diverse types of users. Others require a large amount of previously gathered data about users’ interactions and preferences, in order to be successfully applied. In this study, we introduce a methodology for the creation of Personalized Multi Criteria Context Aware Recommender Systems that aims to overcome these shortcomings. Our methodology incorporates the user’s current context information, and techniques from the Multiple Criteria Decision Analysis (MCDA) field of study to analyze and model the user preferences. To this end, we create a multi criteria user preference model to assess the utility of each item for a specific user, to then recommend the items with the highest utility. The criteria considered when creating the user preference model are the user’s location, mobility level and user profile. The latter is obtained by considering the user specific needs, and generalizing the user data from a large scale demographic database. We present a case study where we applied our methodology into PeRS, a personal Recommender System to recommend events that will take place within the Ottawa/Gatineau Region. Furthermore, we conduct an offline experiment performed to evaluate our methodology, as implemented in our case study. From the experimental results we conclude that our RS is capable to accurately narrow down, and identify, the groups from a demographic database where a user may belong, and subsequently generate highly accurate recommendation lists of items that match with his/her preferences. This means that the system has the ability to understand and typify the user. Moreover, the results show that the obtained system accuracy doesn’t depend on the user profile. Therefore, the system is potentially capable to produce equally accurate recommendations for a wide range of the population.

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Feld, Michael [Verfasser], and Wolfgang [Akademischer Betreuer] Wahlster. "A speaker classification framework for non-intrusive user modeling : speech-based personalization of in-car services / Michael Feld. Betreuer: Wolfgang Wahlster." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2012. http://d-nb.info/1052338348/34.

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Walter, Nadine [Verfasser], Klaus [Akademischer Betreuer] Bengler, Albrecht [Gutachter] Schmidt, and Klaus [Gutachter] Bengler. "Personalization and context-sensitive user interaction of in-vehicle infotainment systems / Nadine Walter ; Gutachter: Albrecht Schmidt, Klaus Bengler ; Betreuer: Klaus Bengler." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1194547613/34.

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Meguebli, Youssef. "Leveraging User-Generated Content for Enhancing and Personalizing News Recommendation." Thesis, CentraleSupélec, 2015. http://www.theses.fr/2015SUPL0007/document.

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La motivation principale de cette thèse est de proposer un système de recommandation personnalisé pour les plateformes d’informations. Pour cela, nous avons démontré que les opinions peuvent constituer un descripteur efficace pour améliorer la qualité de la recommandation. Au cours de cette thèse, nous avons abordé ce problème en proposant trois contributions principales. Tout d’abord, nous avons proposé un modèle de profil qui décrit avec précision les intérêts des utilisateurs ainsi que le contenu des articles de presse. Le modèle de profil proposé repose sur trois éléments : les entités nommées, les aspects et les sentiments. Nous avons testé notre modèle de profil sur les trois applications différentes que sont l’identification des orientations politiques des utilisateurs, la recommandation personnalisée des articles de presse et enfin la diversification de la liste des articles recommandés. Deuxièmement, nous avons proposé une approche de classement des opinions permettant de filtrer et sélectionner seulement les opinions pertinentes. Pour cela, nous avons utilisé une variation de la technique de PageRank pour définir le score de chaque opinion. Les résultats montrent que notre approche surpasse deux approches récemment proposées pour le classement des opinions. Troisièmement, nous avons étudié différentes façons d’enrichir le contenu des articles de presse par les opinions : par toutes les opinions, par seulement le topk des opinions, et enfin par un ensemble d’opinions diversifiées. Les résultats montrent que l’enrichissement des contenus des articles de presse In this thesis, we have investigated how to exploit user-generated-content for personalized news recommendation purpose. The intuition behind this line of research is that the opinions provided by users, on news websites, represent a strong indicator about their profiles. We have addressed this problem by proposing three main contributions. Firstly, we have proposed a profile model that accurately describes both users’ interests and news article contents. The profile model was tested on three different applications ranging from identifying the political orientation of users to the context of news recommendation and the diversification of the list of recommended news articles. Results show that our profile model give much better results compared to state-of-the-art models. Secondly, we have investigated the problem of noise on opinions and how we can retrieve only relevant opinions in response to a given query.The proposed opinion ranking strategy is based on users’ debates features. We have used a variation of PageRank technique to define the score of each opinion. Results show that our approach outperforms two recent proposed opinions ranking strategies, particularly for controversial topics. Thirdly, we have investigated different ways of leveraging opinions on news article contents including all opinions, topk opinions based on opinion ranking strategy, and a set of diverse opinion. To extract a list of diverse opinions, we have employed a variation of an existing opinion diversification model. Results show that diverse opinions give the best performance over other leveraging strategies

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MONGE, ROFFARELLO ALBERTO. "End-User Development in the Internet of Things." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2847152.

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West, Saga, and Phatchana Srinin. "Targeted content - a case study on increasing online user engagement." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43346.

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The data generated from the user's activities and surfing online can have a significant impact when used to improve the content of web pages. The user has little time to scan the content of a web page and the motivation to save time is high. Recommendations and personalized content are becoming more and more common and both businesses and consumers seem to benefit from more targeted content. In order to know what recommendations to present to each individual, information about the user is needed and there are many tools to analyze the behaviours of the users online. In this study, we examined if relevant content on the front-page of Visma, a business software provider, can increase the interaction on the front-page and what metrics can be used to measure the interaction. Together with experts in the field we investigated the eAccounting software users’ preferences for targeted recommendations. By having a pop up survey for users of Visma, conducting an A/B test and using a heatmap for the eAccounting users we found out that a more customized front-page makes the user interact more with the page.

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Rönnberg, Sofia. "Persuasive Chatbot Conversations : Towards a Personalized User Experience." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166354.

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Helping drivers improve their driving skills and become safer drivers is a problematic topic. Most drivers have a lacking self-assessment ability and consider themselves above average driving skills. This is believed to be related to the lack of continuous feedback after getting the driver’s license. This has led to initiatives to find alternative ways of coaching drivers toward better self-assessment and thereby toward safer driving. Chatbots and conversational interfaces has received increasing attention over the years and could be technologies that can solve these challenges. However, a major challenge to chatbots is that they are mostly implemented in a “one-size-fits-all” approach, and while personalization of the chatbot could solve that challenge, it ishard to achieve. In this study, personalized chatbot conversations that aim to coach drivers are examined. The aim is to create a guide that can help designers and practitioners with design decisions that needs to be considered when creating coaching chatbot conversations. The study was performed as a Wizard of Oz study, where attributes for personalization as well as coaching considerations were tested with users in two iterations to iteratively develop the guide. The findings of the study include the guide itself with its guidelines (see appendix 4), as well as insights on considerations required chatbot personalization and coaching. Regarding personalization, chatbot personality and level of control were identified as two attributes that were fit for adaptation. These can lead to social benefits as well as more tailored services to the users. For coaching, the use of follow-ups, feedback and the chatbot’s attitude are identified as necessary considerations when designing coaching chatbot conversations.

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Zhao, Jianxin. "Making Digital Libraries Flexible, Scalable and Reliable: Reengineering the MARIAN System in JAVA." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/78146.

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There is a great need for digital libraries that are flexible, scalable, and reliable. Few such systems exist. Little is known about how to build them. This thesis addresses these problems by enhancing a prototype digital library system with the aim of making it more flexible, scalable, and reliable. We hypothesize that: 1) adding a new (user information) layer and maintaining weak coupling in the design of a digital library system can help achieve system flexibility; 2) optimizing network connection usage and facilitating distribution of computation and disk operations in system design can help achieve system scalability; and 3) applying good software processes can help university students produce a very reliable system. Approaches based on the above hypothesis were used in the project of Reengineering the MARIAN System in Java. The results of the project and experiments verified the correctness of the hypothesis. The results of this thesis may help inform future digital library design and implementation projects to produce flexible, scalable, and reliable systems. Master of Science

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Andersson, Reimer Roger. "Relify." Thesis, Malmö högskola, Institutionen för konst, kultur och kommunikation (K3), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22709.

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Mobile phone's offer enriched ways of communicating and have become devices with a very strong personal attachment for most users. While being personal communication devices, mobile phones displays limits with user interfaces more focused on features and distinct applications rather thanproviding rich interactions with a focus on people and communication between people.The present project explores interaction design for the mobile phone as a more personal and social device. It's key outcome is the Relify concept with it's affiliated prototype. Relify is a mobile userinterface where presentation of people and communication is influenced by the character of the relationships with the people involved. Relify creates a personalized and flexible way for users to gain access to both personal communication and web feeds from third party services within aseamless user interface. This report describes the explorative design process leading to Relify, findings from field testing of it's prototype and design themes identified during the work. The work concludes that Relify demonstrates a personalized and fluent user interface with increased overview and a richer userexperience. Among it's experiential qualities is an increased sense of connection, that can excite communication between people. In summarizing the work, considerations and implications ondesigning mobile user interfaces are discussed against the experiences of this project.

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EL-Khoury, Vanessa [Verfasser], and Harald [Akademischer Betreuer] Kosch. "Semantic Protection and Personalization of Video Content. PIAF: MPEG Compliant Adaptation Framework Preserving the User Perceived Quality / Vanessa EL-Khoury. Betreuer: Harald Kosch." Passau : Universitätsbibliothek der Universität Passau, 2014. http://d-nb.info/1058750011/34.

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Holm, Anders, and Kullström Christoffer Sundberg. "User Responsive User Experience Design: Building a Conceptual Framework." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-1036.

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To a large extent, business-customer interactions are acted out on digital meeting places. When the possibilities for businesses to engage in face-to-face interactions decrease, relationship building and customer service becomes more of a challenge. Digital services are easily duplicated by competitors and with standardization of interfaces and products, customers tend to switch more frequently between providers. One area where the creation and maintenance of loyal customers appears to be highly relevant is the domain of e-banking. Studies have shown that by personalizing the experience for the user, customer loyalty can be enhanced. Existing methods of interface adaptation shifts the responsibility for the resulting user experience design from the designer to either the user or the system. However, research shows that handing over responsibility for the design to the user can damage the user experience. Furthermore, we argue that as long as computers cannot translate the meaning of what a human communicates, and understand the motivation that lies behind her actions, human designers and researchers need to own the responsibility for designing user experiences. Responsive web design differ from the concept of user responsiveness in the way that it is not truly responsive to the user but to the technical device that is used. Following a design science research methodology, this paper presents the development of a conceptual framework for user responsive user experience design (URUXD) that aims to strengthen the bond between user and provider by enabling a more relevant and personalized user experience. The conceptual framework introduces a way to design user responsive information systems that could be useful in domains where the user audience is large and diverse, as in the case of e-banking. A personalized user experience is enabled by transcending the current use of personas as design tools to also involve them in categorizing real-time users through the use of personas as mapping tools. Multiple persona sets are incorporated in the framework which gives the user experience designer the possibility of designing a holistic user experience for each persona set. The framework thus enables the incorporation of multiple GUI designs in an information system that is user responsive, without the risk of violating usability principles. Interaktionen mellan företag och kund sker nuförtiden oftast på digitala mötesplatser. När möjligheten för företag att träffa kunden öga mot öga minskar blir det en utmaning att skapa nära affärsrelationer och förmedla bra kundservice. Med konkurrenter som enkelt kopierar digitala tjänster och med en standardisering av gränssnitt och tjänster tenderar kunder att oftare byta leverantör. Inom e-banking framstår därför skapande och upprätthållande av lojala kunder som högst relevant. Genom att personifiera användarupplevelsen kan kundlojaliteten förbättras. Befintliga metoder för gränssnittsadaption lämnar över ansvaret för den resulterande designen från designern till antingen användaren eller systemet. Men, om ansvaret för designen tilldelas användaren kan resultatet bli i en skadad användarupplevelse. Så länge datorer inte kan översätta meningen bakom vad en människa kommunicerar eller skapa en förståelse för en användares bakomliggande motivation till varför hen utför handlingar, måste mänskliga designers inneha ansvaret för designen av användarupplevelsen. Vidare skiljer sig responsiv webbdesign från konceptet användarresponsivitet i meningen att responsiv webbdesign inte är direkt responsiv mot användaren utan snarare mot den tekniska apparat som används. Genom att följa en design science forskningsmetodik utvecklades ett konceptuellt ramverk för design av användarresponsiva användarupplevelser (user responsive user experience design (URUXD)). Målet var att stärka bandet mellan användare och leverantör genom att möjliggöra en mer relevant och personifierad användarupplevelse. Det konceptuella ramverket introducerar ett sätt att designa användarresponsiva informationssystem vilket kan vara användbart i domäner där användargruppen är stor och heterogen, vilket är fallet för e-banking. En personifierad användarupplevelse möjliggörs genom att utöka det befintliga användningsområdet för designverktyget personas till att även inkludera dem som mappningssverktyg för att kategorisera användare i realtid. Multipla persona sets införlivas i ramverket vilket skapar möjlighet för designern att skapa en holistisk användarupplevelse för varje enskilt persona set. Det konceptuella ramverket möjliggör därigenom för multipla gränssnittdesigns för ett informationssystem som därmed blir användarresponsivt, utan att underminera principer för användbarhet.

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Hadjouni, Krir Myriam. "Un système de recherche d’information personnalisée basé sur la modélisation multidimensionnelle de l’utilisateur." Thesis, Paris 11, 2012. http://www.theses.fr/2012PA112164/document.

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Depuis l'explosion du Web, la Recherche d'Information (RI) s'est vue étendue et les moteurs de recherche sur le Web ont vu le jour. Les méthodes classiques de la RI, surtout destinées à des recherches textuelles simples, se sont retrouvées face à des documents de différents formats et des contenus riches. L'utilisateur, en réponse à cette avancée, est devenu plus exigeant quant aux résultats retournés par les systèmes de RI. La personnalisation tente de répondre à ces exigences en ayant pour objectif principal l'amélioration des résultats retournés à l'utilisateur en fonction de sa perception et de ses intérêts ainsi que de ses préférences. Le présent travail de thèse se situe à la croisée des différents aspects présentés et couvre cette problématique. Elle a pour objectif principal de proposer des solutions nouvelles et efficaces à cette problématique. Pour atteindre cet objectif, un système de personnalisation de la recherche spatiale et sémantique sur le Web et intégrant la modélisation de l'utilisateur, a été proposé. Ce système comprend deux volets : 1/ la modélisation de l'utilisateur ; 2/ la collaboration implicite des utilisateurs à travers la construction d'un réseau de modèles utilisateurs, construit itérativement lors des différentes recherches effectuées en ligne. Un prototype supportant le système proposé a été développé afin d'expérimenter et d'évaluer l'ensemble de la proposition. Ainsi, nous avons effectué un ensemble d'évaluation, dont les principales sont : a) l'évaluation de la qualité du modèle de l'utilisateur ; b) l'évaluation de l'efficacité de la recherche d'information ; c) l’évaluation de l'efficacité de la recherche d'information intégrant les informations spatiales ; d) l'évaluation de la recherche exploitant le réseau d'utilisateurs. Les expérimentations menées montrent une amélioration de la personnalisation des résultats présentés par rapport à ceux obtenus par d'autres moteurs de recherche The web explosion has led Information Retrieval (IR) to be extended and web search engines emergence. The conventional IR methods, usually intended for simple textual searches, faced new documents types and rich and scalable contents. The users, facing these evolutions, ask more for IR systems search results quality. In this context, the personalization main objective is improving results returned to the end user based sing on its perception and its interests and preferences. This thesis context is concerned with these different aspects. Its main objective is to propose new and effective solutions to the personalization problem. To achieve this goal, a spatial and semantic web personalization system integrating implicit user modeling is proposed. This system has two components: 1/ user modeling; /2 implicit users' collaboration through the construction of a users' models network. A system prototype was developed for the evaluation purpose that contains: a) user model quality evaluation; b) information retrieval quality evaluation; c) information retrieval quality evaluation with the spatial user model data; d) information retrieval quality evaluation with the whole user model data and the users' models network. Experiments showed amelioration in the personalized search results compared to a baseline web search

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BASSANI, ELIAS. "Neural Approaches to Personalized Search." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2023. https://hdl.handle.net/10281/404515.

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I recenti progressi nella ricerca sulle Artificial Neural Networks (reti neurali) hanno fatto avanzare lo stato dell'arte in molti task legati al linguaggio, tra cui l'Information Retrieval, offrendo nuove opportunità per rappresentare e sfruttare le informazioni relative all'utente durante la personalizzazione. Tuttavia, la loro applicazione nel contesto della Personalized Search è ancora un'area di ricerca aperta, con molte questioni e sfide da affrontare. In questa tesi, ci concentriamo sulla rappresentazione delle preferenze dell'utente da più prospettive, sulla gestione e sulla selezione delle informazioni dell'utente per personalizzare la ricerca corrente e sul miglioramento della rappresentazione delle query con dati specifici dell'utente, proponendo nuovi approcci basati sulle reti neurali. Inoltre, affrontiamo il problema della mancanza di grandi dataset condivisi pubblicamente, adatti all'addestramento e alla valutazione di approcci basati su reti neurali per la ricerca personalizzata. In primo luogo, studiamo il problema di sfruttare le preferenze degli utenti rappresentate da più prospettive, proponendo un modello di ri-ranking multi-rappresentazione. Dimostriamo che l'approccio proposto raggiunge prestazioni competitive, è efficiente, scalabile e può essere esteso per includere rappresentazioni ed informazioni aggiuntive. In seguito, abbiamo condotto un'analisi approfondita di un meccanismo delle reti neurali, l'Attention, quando viene impiegato per la modellazione degli utenti, evidenziando alcune carenze dovute a uno dei suoi componenti interni, la funzione di normalizzazione Softmax. Per ovviare a tali carenze, abbiamo introdotto una nuova variante dell'Attention, l'a Denoising Attention, che adotta uno schema di normalizzazione più robusto e impiega un meccanismo di filtraggio. Le valutazioni sperimentali mostrano chiaramente i vantaggi dell'approccio proposto rispetto alle altre varianti di Attention. Inoltre, ci occupiamo del miglioramento delle rappresentazioni delle query con dati specifici dell'utente, proponendo un nuovo approccio di Query Expansion personalizzata progettato per i contextual word embedding, che sfrutta una procedura offline basata sul clustering per identificare i termini correlati all'utente che meglio rappresentano i suoi interessi. Dimostriamo che migliora in termini di efficacia di recupero rispetto ai metodi di Query Expansion basati su word embedding allo stato dell'arte, ottenendo anche tempi di espansione inferiori al millisecondo grazie a un'approssimazione da noi proposta. Infine, discutiamo lo stato della valutazione dell'Information Retrieval personalizzato e i dataset disponibili pubblicamente e proponiamo e condividiamo un nuovo benchmark su larga scala per quattro domini, con oltre 18 milioni di documenti e 1,9 milioni di query. Presentiamo una descrizione dettagliata della procedura di costruzione del benchmark, evidenziandone le caratteristiche e le sfide, e forniamo delle linee guida per i lavori futuri. Le soluzioni e i risultati presentati in questa tesi dimostrano che la ricerca personalizzata è un'area di ricerca ancora aperta. Inoltre, le nuove opportunità offerte dai recenti progressi delle reti neurali introducono anche nuove sfide che devono essere affrontate correttamente per sfruttare appieno il loro potenziale e renderle utili per le applicazioni di ricerca personalizzata del mondo reale. The recent advancements in Neural Networks research have pushed forward the state-of-the-art in many language-related tasks, including Information Retrieval, bringing new opportunities for representing and leveraging user-related information during personalization. However, their application in the context of Personalized Search is still an open research area, with many issues and challenges to be addressed and tackled. In this dissertation, we focus on representing the user preferences from multiple perspectives, managing and selecting the user information to personalize the current search, and improving query representations with user-specific data by proposing new approaches based on Neural Networks. Moreover, we address the lack of publicly available large-scale datasets suited for training and evaluating Neural Networks-based approaches for Personalized Search. We first study the problem of leveraging the user preferences represented from multiple perspectives by proposing a multi-representation re-ranking model. We show that our proposed approach achieves competitive performance while being fast, scalable, and extended to include additional representations and features. We then conduct an in-depth analysis of a Neural Networks mechanism, the Attention, when employed for user modeling, highlighting some shortcomings due to one of its internal components, the Softmax normalization function. We address those shortcomings by introducing a novel Attention variant, the Denoising Attention, that adopts a more robust normalization scheme and employs a filtering mechanism. Experimental evaluations clearly show the benefits of our proposed approach over other Attention variants. Furthermore, we address the enhancement of query representations with user-specific data by proposing a novel Personalized Query Expansion approach designed for contextualized word embeddings, which leverages an offline clustering-based procedure to identify the user-related terms that better represent the user interests. We show it improves in terms of retrieval effectiveness over word embedding-based Query Expansion methods at the state-of-the-art while also achieving sub-millisecond expansion time thanks to an approximation we propose. Finally, we discuss the state of Personalized Information Retrieval evaluation and the available publicly available datasets and propose and share a novel large-scale benchmark across four domains, with more than 18 million documents and 1.9 million queries. We present a detailed description of the benchmark construction procedure, highlighting its characteristics and challenges, and provide baselines for future works. The solutions and findings presented in this dissertation show that Personalized Search is still an open research area. Moreover, the new opportunities brought to the table by the recent advancements in Neural Networks also introduce new challenges that need to be correctly addressed to both take full advantage of their potential and make them valuable for real-world Personalized Search applications.

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Kapanipathi, Pavan. "Personalized and Adaptive Semantic Information Filtering for Social Media." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464541093.

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Johansson, Axel, and Jonas Wikström. "Customer segmentation using machine learning." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-443868.

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In this thesis, the process of developing an application for segmenting customers with the use of machine learning is described. The project was carried out at a company which provides a booking platform for beauty and health services. Data about customers were analyzed and processed in order to train two classification models able to segment customers into three different customer groups. The performance of the two models, a Logistic Regression model and a Support Vector Classifier, were evaluated with different numbers of features and compared to classifications made by human experts working at the company. The results shows that the logistic regression model achieved an accuracy of 71% when classifying users into the three groups, which was more accurate than the experts manual classification. A web API where the model is provided has been developed and presented to the company. The results of the study showed that machine learning is a useful technique for performing customer segmentation based on behavioral data. Even in the case where the classes are not naturally divisible, the application provides valuable insights on user behaviour that can help the company become more data-driven.

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Wiklund, Ida. "A Recommendation system for News Push Notifications- Personalizing with a User-based and Content-based Recommendation system." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172275.

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The news landscape has changed during recent years because of the digitization. News can nowadays be found in both newspapers and on different sites online. The availability of the digital newspapers leads to competition among the news companies. To make the users stay on one specific platform for news, relevance is required in the content and oneway of creating relevance is through personalization, to tailor the content to each user. The focus of this thesis is therefore personalizing newspush notifications for a digital newspaper and making them more relevant for users. The project was made in cooperation with VK Media, and their digital newspaper. The task in this thesis is to implement personalization of push notifications by building a recommendation system and to test the implemented system with data from VK. In order to perform the task, a dataset representing reading habits of VK’s users was extracted from their data warehouse. Then a user-based and content-based recommendation system was implemented in Python.The idea with the system is to recommend new articles that are sufficiently similar to one or more of the already read articles. Articles that may be liked by one of the most similar users should also be recommended. Finally, the system’s performance was evaluated with the data representing reading habits for VK’s users. The results show that the implemented system has better performance than the current solution without any personalization, when recommending a few articles to each user. The results from the evaluation also show that the more articles the users have read, the better predictions are possible to make. Thus, this thesis offers a first step towards meeting the expectations of more relevant content among VK’s users.

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Gil, Pascual Miriam. "Adapting Interaction Obtrusiveness: Making Ubiquitous Interactions Less Obnoxious. A Model Driven Engineering approach." Doctoral thesis, Universitat Politècnica de València, 2013. http://hdl.handle.net/10251/31660.

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La Computaci'on Ubicua plantea proveer de inteligencia a nuestros entornos ofreciendo servicios a los usuarios que permitan ayudarlos en su vida cotidiana. Con la inclusi'on de dispositivos ubicuos en nuestra vida (por ejemplo los dispositivos m'oviles), los usuarios hemos pasado a estar siempre conectados al entorno, pudiendo interactuar con el. Sin embargo, a diferencia de las interacciones de escritorio tradicionales donde los usuarios eran quienes ped'¿an informaci'on o introduc'¿an datos, las interacciones ubicuas tienen que lidiar con un entorno de los usuarios variable, demandando uno de los recursos mas valiosos para los usuarios: la atenci'on humana. De esta forma, un reto en el paradigma de computaci'on ubicua es regular las peticiones de atenci'on del usuario. Esto implica que las interacciones de los servicios deber'¿an comportarse de una manera ¿considerada¿ teniendo en cuenta el grado en que cada servicio se inmiscuye en la mente del usuario (el nivel de molestia). Partiendo de las bases de la Ingenier'¿a Dirigida por Modelos (MDE) y de los principios de la Computaci'on Considerada, esta tesis se orienta a dise¿nar y desarrollar servicios que sean capaces de adaptar sus interacciones de acuerdo a la atenci'on del usuario en cada momento. El principal objetivo de esta tesis es introducir capacidades de adaptaci'on considerada en los servicios ubicuos para proporcionar interacciones que no perturben al usuario. Esto lo conseguimos mediante un proceso de desarrollo que cubre desde el dise¿no de los servicios hasta su implementaci'on, centr'andose en los requisitos de adaptaci'on de la interacci'on particulares para cada usuario. Para el dise¿no del comportamiento de la interacci'on en base al nivel de molestia se han de¿nido unos modelos de intromisi'on e interacci'on independientes de la tecnolog'¿a. Estos modelos son los que posteriormente conducen la adaptaci'on de la interacci'on din'amicamente, por medio de una infraestructura aut'onoma que los usa en tiempo de ejecuci'on. Esta infraestructura es capaz de detectar cambios en la situaci'on del usuario (por ejemplo cambios en su localizaci'on, su actividad, etc.) y planear y ejecutar modi¿caciones en la interacci'on de los servicios. Cuando se detecta un cambio del contexto del usuario, los servicios se auto-adaptan para usar los componentes de interacci'on m'as apropiados de acuerdo a la nueva situaci'on y no molestar al usuario. Adem'as, como las necesidades y preferencias de los usuarios pueden cambiar con el tiempo, nuestra aproximaci'on utiliza la estrategia del aprendizaje por refuerzo para ajustar los modelos de dise¿no iniciales de forma que maximicemos la experiencia del usuario. El dise¿no inicial de la interacci'on basado en el nivel de molestia nos asegura un comportamiento inicial consistente con las necesidades de los usuarios en ese momento. Luego, este dise¿no se va re¿nando de acuerdo al comportamiento y preferencias de cada usuario por medio de su retroalimentaci'on a trav'es de la experiencia de uso. Adem'as, tambi'en proporcionamos una interfaz m'ovil que permite a los usuarios ¿nales personalizarse de forma manual los modelos en base a sus propias preferencias. El trabajo presentado en esta tesis se ha llevado a la pr'actica para su evaluaci'on desde el punto de vista de los dise¿nadores y de los usuarios ¿nales. Por una parte, el m'etodo de dise¿no se ha validado para comprobar que ayuda a los dise¿nadores a especi¿car este tipo de servicios. Pese a que el proceso de desarrollo no ofrece una automatizaci'on completa, las gu'¿as ofrecidas y la formalizaci'on de los conceptos implicados ha demostrado ser 'util a la hora de desarrollar servicios cuya interacci'on es no molesta. Por otra parte, la adaptaci'on de la interacci'on en base al nivel de molestia se ha puesto en pr'actica con usuarios para evaluar su satisfacci'on con el sistema y su experiencia de usuario. Esta validaci'on ha desvelado la importancia de considerar los aspectos de molestia en el proceso de adaptaci'on de la interacci'on para ayudar a mejorar la experiencia de usuario. In Ubiquitous Computing environments, people are surrounded by a lot of embedded services. Since ubiquitous devices, such as mobile phones, have become a key part of our everyday life, they enable users to be always connected to the environment and interact with it. However, unlike traditional desktop interactions where users are used to request for information or input data, ubiquitous interactions have to face with variable user¿s environment, making demands on one of the most valuable resources of users: human attention. A challenge in the Ubiquitous Computing paradigm is regulating the request for user¿s attention. That is, service interactions should behave in a considerate manner by taking into account the degree in which each service intrudes the user¿s mind (i.e., the obtrusiveness degree). In order to prevent service behavior from becoming overwhelming, this work, based on Model Driven Engineering foundations and the Considerate Computing principles, is devoted to design and develop services that adapt their interactions according to user¿s attention. The main goal of the present thesis is to introduce considerate adaptation capabilities in ubiquitous services to provide non-disturbing interactions. We achieve this by means of a systematic method that covers from the services¿ design to their implementation and later adaptation of interaction at runtime Gil Pascual, M. (2013). Adapting Interaction Obtrusiveness: Making Ubiquitous Interactions Less Obnoxious. A Model Driven Engineering approach [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31660 TESIS

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Osmanli, Osman Nuri. "A Singular Value Decomposition Approach For Recommendation Systems." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612129/index.pdf.

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Data analysis has become a very important area for both companies and researchers as a consequence of the technological developments in recent years. Companies are trying to increase their profit by analyzing the existing data about their customers and making decisions for the future according to the results of these analyses. Parallel to the need of companies, researchers are investigating different methodologies to analyze data more accurately with high performance. Recommender systems are one of the most popular and widespread data analysis tools. A recommender system applies knowledge discovery techniques to the existing data and makes personalized product recommendations during live customer interaction. However, the huge growth of customers and products especially on the internet, poses some challenges for recommender systems, producing high quality recommendations and performing millions of recommendations per second. In order to improve the performance of recommender systems, researchers have proposed many different methods. Singular Value Decomposition (SVD) technique based on dimension reduction is one of these methods which produces high quality recommendations, but has to undergo very expensive matrix calculations. In this thesis, we propose and experimentally validate some contributions to SVD technique which are based on the user and the item categorization. Besides, we adopt tags to classical 2D (User-Item) SVD technique and report the results of experiments. Results are promising to make more accurate and scalable recommender systems.

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Dissertations / Theses on the topic 'User Personalization'

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