To see the other types of publications on this topic, follow the link: Recommendation algorithms.
Dissertations / Theses on the topic 'Recommendation algorithms'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Recommendation algorithms.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
VASILOUDIS, THEODOROS. "Extending recommendation algorithms bymodeling user context." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-156306.
Full textAbstract:
Recommender systems have been widely adopted by onlinee-commerce websites like Amazon and music streaming services like Spotify. However, most research efforts have not sufficiently considered the context in which recommendations are made, especially when the input is implicit. In this work, we investigate the value of including contextual information like day-of-week in collaborative filtering recommender systems. For the investigation, we first implemented two algorithms, namely contextual prefiltering and contextual post-filtering. Then, we evaluated these algorithms with user data collected from Spotify. Experiment results show that the pre-filtering algorithm shows some promise against an item similarity baseline, indicating that further investigation could be rewarding. The post-filtering algorithm underperforms a popularity-derived baseline, due to information loss in the recommendationprocess.
2
Viviani, Giovanni. "Optimizing modern code review through recommendation algorithms." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58757.
Full textAbstract:
Software developers have many tools at their disposal that use a variety of sophisticated technology, such as static analysis and model checking, to help find defects before software is released. Despite the availability of such tools, software development still relies largely on human inspection of code to find defects. Many software development projects use code reviews as a means to ensure this human inspection occurs before a commit is merged into the system. Known as modern code review, this approach is based on tools, such as Gerrit, that help developers track commits for which review is needed and that help perform reviews asynchronously. As part of this approach, developers are often presented with a list of open code reviews requiring attention. Existing code review tools simply order this list of open reviews based on the last update time of the review; it is left to a developer to find a suitable review on which to work from a long list of reviews. In this thesis, we present an investigation of four algorithms that recommend an ordering of the list of open reviews based on properties of the reviews. We use a simulation study over a dataset of six projects from the Eclipse Foundation to show that an algorithm based on ordering reviews from least lines of code modified in the changes to be reviewed to most lines of code modified out performs other algorithms. This algorithm shows promise for eliminating stagnation of reviews and optimizing the average duration reviews are open.Science, Faculty of
Computer Science, Department of
Graduate
3
Casey, Walker Evan. "Scalable Collaborative Filtering Recommendation Algorithms on Apache Spark." Scholarship @ Claremont, 2014. http://scholarship.claremont.edu/cmc_theses/873.
Full textAbstract:
Collaborative filtering based recommender systems use information about a user's preferences to make personalized predictions about content, such as topics, people, or products, that they might find relevant. As the volume of accessible information and active users on the Internet continues to grow, it becomes increasingly difficult to compute recommendations quickly and accurately over a large dataset. In this study, we will introduce an algorithmic framework built on top of Apache Spark for parallel computation of the neighborhood-based collaborative filtering problem, which allows the algorithm to scale linearly with a growing number of users. We also investigate several different variants of this technique including user and item-based recommendation approaches, correlation and vector-based similarity calculations, and selective down-sampling of user interactions. Finally, we provide an experimental comparison of these techniques on the MovieLens dataset consisting of 10 million movie ratings.
4
Lisena, Pasquale. "Knowledge-based music recommendation : models, algorithms and exploratory search." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS614.
Full textAbstract:
Représenter l'information décrivant la musique est une activité complexe, qui implique différentes sous-tâches. Ce manuscrit de thèse porte principalement sur la musique classique et étudie comment représenter et exploiter ses informations. L'objectif principal est l'étude de stratégies de représentation et de découverte des connaissances appliquées à la musique classique, dans des domaines tels que la production de base de connaissances, la prédiction de métadonnées et les systèmes de recommandation. Nous proposons une architecture pour la gestion des métadonnées de musique à l'aide des technologies du Web Sémantique. Nous introduisons une ontologie spécialisée et un ensemble de vocabulaires contrôlés pour les différents concepts spécifiques à la musique. Ensuite, nous présentons une approche de conversion des données, afin d’aller au-delà de la pratique bibliothécaire actuellement utilisée, en s’appuyant sur des règles de mapping et sur l’interconnexion avec des vocabulaires contrôlés. Enfin, nous montrons comment ces données peuvent être exploitées. En particulier, nous étudions des approches basées sur des plongements calculés sur des métadonnées structurées, des titres et de la musique symbolique pour classer et recommander de la musique. Plusieurs applications de démonstration ont été réalisées pour tester les approches et les ressources précédentesRepresenting the information about music is a complex activity that involves different sub-tasks. This thesis manuscript mostly focuses on classical music, researching how to represent and exploit its information. The main goal is the investigation of strategies of knowledge representation and discovery applied to classical music, involving subjects such as Knowledge-Base population, metadata prediction, and recommender systems. We propose a complete workflow for the management of music metadata using Semantic Web technologies. We introduce a specialised ontology and a set of controlled vocabularies for the different concepts specific to music. Then, we present an approach for converting data, in order to go beyond the librarian practice currently in use, relying on mapping rules and interlinking with controlled vocabularies. Finally, we show how these data can be exploited. In particular, we study approaches based on embeddings computed on structured metadata, titles, and symbolic music for ranking and recommending music. Several demo applications have been realised for testing the previous approaches and resources
5
Asebedo, Antonio Ray. "Development of sensor-based nitrogen recommendation algorithms for cereal crops." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/19229.
Full textAbstract:
Doctor of PhilosophyDepartment of Agronomy
David B. Mengel
Nitrogen (N) management is one of the most recognizable components of farming both within and outside the world of agriculture. Interest over the past decade has greatly increased in improving N management systems in corn (Zea mays) and winter wheat (Triticum aestivum) to have high NUE, high yield, and be environmentally sustainable. Nine winter wheat experiments were conducted across seven locations from 2011 through 2013. The objectives of this study were to evaluate the impacts of fall-winter, Feekes 4, Feekes 7, and Feekes 9 N applications on winter wheat grain yield, grain protein, and total grain N uptake. Nitrogen treatments were applied as single or split applications in the fall-winter, and top-dressed in the spring at Feekes 4, Feekes 7, and Feekes 9 with applied N rates ranging from 0 to 134 kg ha[superscript]-1. Results indicate that Feekes 7 and 9 N applications provide more optimal combinations of grain yield, grain protein levels, and fertilizer N recovered in the grain when compared to comparable rates of N applied in the fall-winter or at Feekes 4. Winter wheat N management studies from 2006 through 2013 were utilized to develop sensor-based N recommendation algorithms for winter wheat in Kansas. Algorithm RosieKat v.2.6 was designed for multiple N application strategies and utilized N reference strips for establishing N response potential. Algorithm NRS v1.5 addressed single top-dress N applications and does not require a N reference strip. In 2013, field validations of both algorithms were conducted at eight locations across Kansas. Results show algorithm RK v2.6 consistently provided highly efficient N recommendations for improving NUE, while achieving high grain yield and grain protein. Without the use of the N reference strip, NRS v1.5 performed statistically equal to the KSU soil test N recommendation in regards to grain yield but with lower applied N rates. Six corn N fertigation experiments were conducted at KSU irrigated experiment fields from 2012 through 2014 to evaluate the previously developed KSU sensor-based N recommendation algorithm in corn N fertigation systems. Results indicate that the current KSU corn algorithm was effective at achieving high yields, but has the tendency to overestimate N requirements. To optimize sensor-based N recommendations for N fertigation systems, algorithms must be specifically designed for these systems to take advantage of their full capabilities, thus allowing implementation of high NUE N management systems.
6
Li, Lei. "Next Generation of Recommender Systems: Algorithms and Applications." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1446.
Full textAbstract:
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.
7
Dhumal, Sayali. "WEB APPLICATION FOR GRADUATE COURSE RECOMMENDATION SYSTEM." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/605.
Full textAbstract:
The main aim of the course advising system is to build a course recommendation path for students to help them plan courses to successfully graduate on time. The recommendation path displays the list of courses a student can take in each quarter from the first quarter after admission until the graduation quarter. The courses are filtered as per the student’s interest obtained from a questionnaire asked to the student.
The business logic involves building the recommendation algorithm. Also, the application is functionality-tested end-to-end by using nightwatch.js which is built on top of node.js. Test cases are written for every module and implemented while building the application.
8
Nicol, Olivier. "Data-driven evaluation of contextual bandit algorithms and applications to dynamic recommendation." Thesis, Lille 1, 2014. http://www.theses.fr/2014LIL10211/document.
Full textAbstract:
Ce travail de thèse a été réalisé dans le contexte de la recommandation dynamique. La recommandation est l'action de fournir du contenu personnalisé à un utilisateur utilisant une application, dans le but d'améliorer son utilisation e.g. la recommandation d'un produit sur un site marchant ou d'un article sur un blog. La recommandation est considérée comme dynamique lorsque le contenu à recommander ou encore les goûts des utilisateurs évoluent rapidement e.g. la recommandation d'actualités. Beaucoup d'applications auxquelles nous nous intéressons génèrent d'énormes quantités de données grâce à leurs millions d'utilisateurs sur Internet. Néanmoins, l'utilisation de ces données pour évaluer une nouvelle technique de recommandation ou encore comparer deux algorithmes de recommandation est loin d'être triviale. C'est cette problématique que nous considérons ici. Certaines approches ont déjà été proposées. Néanmoins elles sont très peu étudiées autant théoriquement (biais non quantifié, borne de convergence assez large...) qu'empiriquement (expériences sur données privées). Dans ce travail nous commençons par combler de nombreuses lacunes de l'analyse théorique. Ensuite nous discutons les résultats très surprenants d'une expérience à très grande échelle : une compétition ouverte au public que nous avons organisée. Cette compétition nous a permis de mettre en évidence une source de biais considérable et constamment présente en pratique : l'accélération temporelle. La suite de ce travail s'attaque à ce problème. Nous montrons qu'une approche à base de bootstrap permet de réduire mais surtout de contrôler ce biaisThe context of this thesis work is dynamic recommendation. Recommendation is the action, for an intelligent system, to supply a user of an application with personalized content so as to enhance what is refered to as "user experience" e.g. recommending a product on a merchant website or even an article on a blog. Recommendation is considered dynamic when the content to recommend or user tastes evolve rapidly e.g. news recommendation. Many applications that are of interest to us generates a tremendous amount of data through the millions of online users they have. Nevertheless, using this data to evaluate a new recommendation technique or even compare two dynamic recommendation algorithms is far from trivial. This is the problem we consider here. Some approaches have already been proposed. Nonetheless they were not studied very thoroughly both from a theoretical point of view (unquantified bias, loose convergence bounds...) and from an empirical one (experiments on private data only). In this work we start by filling many blanks within the theoretical analysis. Then we comment on the result of an experiment of unprecedented scale in this area: a public challenge we organized. This challenge along with a some complementary experiments revealed a unexpected source of a huge bias: time acceleration. The rest of this work tackles this issue. We show that a bootstrap-based approach allows to significantly reduce this bias and more importantly to control it
9
Yang, Fan [Verfasser]. "Analysis, Design and Implementation of Personalized Recommendation Algorithms Supporting Self-organized Communities / Fan Yang." Hagen : Fernuniversität Hagen, 2009. http://d-nb.info/1034265822/34.
Full text
10
Qadeer, Shahab. "Integration of Recommendation and Partial Reference Alignment Algorithms in a Session based Ontology Alignment System." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-73135.
Full textAbstract:
SAMBO is a system to assist users for alignment and merging of two ontologies (i.e. to find inter-ontology relationship). The user performs an alignment process with the help of mapping suggestions. The objective of the thesis work is to extend the existing system with new components; multiple sessions, integration of an ontology alignment strategy, recommendation system, integration of a system that can use results from previous sessions, and integration of partial reference alignment (PRA) that can be used to filter mapping suggestions. Most of the theoretical work existed, but it was important to study and implement, how these components can be integrated in the system, and how they can work together.
11
Safran, Mejdl Sultan. "EFFICIENT LEARNING-BASED RECOMMENDATION ALGORITHMS FOR TOP-N TASKS AND TOP-N WORKERS IN LARGE-SCALE CROWDSOURCING SYSTEMS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1511.
Full textAbstract:
A pressing need for efficient personalized recommendations has emerged in crowdsourcing systems. On the one hand, workers confront a flood of tasks, and they often spend too much time to find tasks matching their skills and interests. Thus, workers want effective recommendation of the most suitable tasks with regard to their skills and preferences. On the other hand, requesters sometimes receive results in low-quality completion since a less qualified worker may start working on a task before a better-skilled worker may get hands on. Thus, requesters want reliable recommendation of the best workers for their tasks in terms of workers' qualifications and accountability. The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers' capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches (mostly developed for e-commerce markets) no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this research, we reveal our insight into the essential difference between the tasks in crowdsourcing systems and the products/items in e-commerce markets, and the difference between buyers' interests in products/items and workers' interests in tasks. Our insight inspires us to bring up categories as a key mediation mechanism between workers and tasks. We propose a two-tier data representation scheme (defining a worker-category suitability score and a worker-task attractiveness score) to support personalized task and worker recommendation. We also extend two optimization methods, namely least mean square error (LMS) and Bayesian personalized rank (BPR) in order to better fit the characteristics of task/worker recommendation in crowdsourcing systems. We then integrate the proposed representation scheme and the extended optimization methods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top-N recommendation algorithms for crowdsourcing systems: (1) Top-N-Tasks (TNT) recommendation algorithms for discovering the top-N most suitable tasks for a given worker, and (2) Top-N-Workers (TNW) recommendation algorithms for identifying the top-N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.
12
Rashidi, Bahman. "Smartphone User Privacy Preserving through Crowdsourcing." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5540.
Full textAbstract:
In current Android architecture, users have to decide whether an app is safe to use or not. Expert users can make savvy decisions to avoid unnecessary private data breach. However, the majority of regular users are not technically capable or do not care to consider privacy implications to make safe decisions. To assist the technically incapable crowd, we propose a permission control framework based on crowdsourcing. At its core, our framework runs new apps under probation mode without granting their permission requests up-front. It provides recommendations on whether to accept or not the permission requests based on decisions from peer expert users. To seek expert users, we propose an expertise rating algorithm using a transitional Bayesian inference model. The recommendation is based on aggregated expert responses and their confidence level. As a complete framework design of the system, this thesis also includes a solution for Android app risks estimation based on behaviour analysis. To eliminate the negative impact from dishonest app owners, we also proposed a bot user detection to make it harder to utilize false recommendations through bot users to impact the overall recommendations. This work also covers a multi-view permission notification design to customize the app safety notification interface based on users' need and an app recommendation method to suggest safe and usable alternative apps to users.
13
Varela, Daniela Renee. "The Netflix Experience : Reshaping the Creative Process: Cultural Co-Production of Content: A user-focus approach to recommendation algorithms." Thesis, Södertörns högskola, Medie- och kommunikationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-33088.
Full textAbstract:
This project proposes a user-focused approach to study the algorithm logic of on-demand apps, using Netflix as a case study. The main research interest is the perception that the user has about the suggestion and recommendation logic of Netflix. In order to gather the information, a walkthrough method on Netflix was applied as well as personal, in-depth think aloud interviews were carried out. The sample consisted on a selection of heavy users, millennials ex-pats living in Singapore and working in the creative industry to get specific insights on their relationship with the algorithm. To analyze the gathered material, qualitative content analysis was carried out. This kind of study is important within today’s contemporary media environment to have integral approach to users perceptions instead of just analytical figures and numbers. The theoretical context used to enlight some of the conclusions discussed on this research were based on the study of media in everyday life, global cultural industry studies, as well as algorithm culture and the science and technology studies. How algorithms are perceived have major repercussions not only on on-demand apps, technology business models or entertainment industry but also an intense influence on the way people consume content. Re-thinking the user as a co-producer of information and knowledge, considering some of the implications this phenomenon might have on the creative industry and how that affects on our daily life are some of the issues this research elaborated on. It can be said that the selected sample appreciates the suggestion logics and it has multiple functionalities: recommendation, curation, entertainment, companionship and leisure. Netflix Originals are very well validated; being one of the main attractions of the app. Interface, functionality and features are also items that the sample positively highlights. The accuracy perception of the algorithm is good, although low when compared to other countries where the sample used the app. The same applies to the amount of content and titles available, being these last two, issues that Netflix could improve. This research was conducted for 8 months, from October 2016 to May 2017, for Sodertorn University – Stockholm, Sweden, with the guidance and support of Associate Professor Anne Kaun.
14
Soualah, Alila Fayrouz. "CAMLearn* : une architecture de système de recommandation sémantique sensible au contexte : application au domaine du m-learning." Thesis, Dijon, 2015. http://www.theses.fr/2015DIJOS032/document.
Full textAbstract:
Au vu de l'émergence rapide des nouvelles technologies mobiles et la croissance des offres et besoins d'une société en mouvement en formation, les travaux se multiplient pour identifier de nouvelles plateformes d'apprentissage pertinentes afin d'améliorer et faciliter le processus d'apprentissage à distance. La prochaine étape de l'apprentissage à distance est naturellement le port de l'apprentissage électronique vers les nouveaux systèmes mobiles. On parle alors de m-learning (apprentissage mobile). Jusqu'à présent l'environnement d'apprentissage était soit défini par un cadre pédagogique soit imposé par le contenu d'apprentissage. Maintenant, nous cherchons, à l'inverse, à adapter le cadre pédagogique et le contenu d'apprentissage au contexte de l'apprenant.Nos travaux de recherche portent sur le développement d'une nouvelle architecture pour le m-learning. Nous proposons une approche pour un système m-learning contextuel et adaptatif intégrant des stratégies de recommandation de scénarios de formations sans risque de ruptureGiven the rapid emergence of new mobile technologies and the growth of needs of a moving society in training, works are increasing to identify new relevant educational platforms to improve distant learning. The next step in distance learning is porting e-learning to mobile systems. This is called m-learning. So far, learning environment was either defined by an educational setting, or imposed by the educational content. In our approach, in m-learning, we change the paradigm where the system recommends content and adapts learning follow to learner's context
15
Holländer, John. "Investigating the performance of matrix factorization techniques applied on purchase data for recommendation purposes." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20624.
Full textAbstract:
Automated systems for producing product recommendations to users is a relatively new area within the field of machine learning. Matrix factorization techniques have been studied to a large extent on data consisting of explicit feedback such as ratings, but to a lesser extent on implicit feedback data consisting of for example purchases.The aim of this study is to investigate how well matrix factorization techniques perform compared to other techniques when used for producing recommendations based on purchase data. We conducted experiments on data from an online bookstore as well as an online fashion store, by running algorithms processing the data and using evaluation metrics to compare the results. We present results proving that for many types of implicit feedback data, matrix factorization techniques are inferior to various neighborhood- and association rules techniques for producing product recommendations. We also present a variant of a user-based neighborhood recommender system algorithm \textit{(UserNN)}, which in all tests we ran outperformed both the matrix factorization algorithms and the k-nearest neighbors algorithm regarding both accuracy and speed. Depending on what dataset was used, the UserNN achieved a precision approximately 2-22 percentage points higher than those of the matrix factorization algorithms, and 2 percentage points higher than the k-nearest neighbors algorithm. The UserNN also outperformed the other algorithms regarding speed, with time consumptions 3.5-5 less than those of the k-nearest neighbors algorithm, and several orders of magnitude less than those of the matrix factorization algorithms.
16
Curnalia, James W. "The Impact of Training Epoch Size on the Accuracy of Collaborative Filtering Models in GraphChi Utilizing a Multi-Cyclic Training Regimen." Youngstown State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1370016838.
Full text
17
Krestel, Ralf [Verfasser]. "On the use of language models and topic models in the web : new algorithms for filtering, classification, ranking, and recommendation / Ralf Krestel." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2012. http://d-nb.info/1022753363/34.
Full text
18
Karrolla, Sanjay. "WEB APPLICATION FOR GRADUATE COURSE ADVISING SYSTEM." CSUSB ScholarWorks, 2017. https://scholarworks.lib.csusb.edu/etd/606.
Full textAbstract:
The main aim of the course recommendation system is to build a course recommendation path for students to help them plan courses to successfully graduate on time. The Model-View-Controller (MVC) architecture is used to isolate the user interface (UI) design from the business logic. The front-end of the application develops the UI using AngularJS. The front-end design is done by gathering the functionality system requirements -- input controls, navigational components, informational components and containers and usability testing. The back-end of the application involves setting up the database and server-side routing. Server-side routing is done using Express JS.
19
Kaufman, Jaime C. "A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features." UNF Digital Commons, 2014. http://digitalcommons.unf.edu/etd/540.
Full textAbstract:
Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering.
Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and its accuracy improves as its pool of users expands. However, the weakness with this approach is the Cold Start problem. It is difficult to recommend items that are either brand new or have no user activity.
Systems that use content-based filtering recommend items based on extracted information from the actual content. A popular example of this approach is Pandora Internet Radio. This approach overcomes the Cold Start problem. However, the main issue with this approach is its heavy demand on computational power. Also, the semantic meaning of an item may not be taken into account when producing recommendations.
In this thesis, a hybrid approach is proposed by utilizing the strengths of both collaborative and content-based filtering techniques. As proof-of-concept, a hybrid music recommendation system was developed and evaluated by users. The results show that this system effectively tackles the Cold Start problem and provides more variation on what is recommended.
20
Schröder, Anna Marie. "Unboxing The Algorithm : Understandability And Algorithmic Experience In Intelligent Music Recommendation Systems." Thesis, Malmö universitet, Institutionen för konst, kultur och kommunikation (K3), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43841.
Full textAbstract:
After decades of black-boxing the existence of algorithms in technologies of daily need, users lack confidence in handling them. This thesis study investigates the use situation of intelligent music recommendation systems and explores how understandability as a principle drawn from sociology, design, and computing can enhance the algorithmic experience. In a Research-Through-Design approach, the project conducted focus user sessions and an expert interview to explore first-hand insights. The analysis showed that users had limited mental models so far but brought curiosity to learn. Explorative prototyping revealed that explanations could improve the algorithmic experience in music recommendation systems. Users could comprehend information the best when it was easy to access and digest, directly related to user behavior, and gave control to correct the algorithm. Concluding, trusting users with more transparent handling of algorithmic workings might make authentic recommendations from intelligent systems applicable in the long run.
21
Zhang, Yi. "Groupwise Distance Learning Algorithm for User Recommendation Systems." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1471347509.
Full text
22
Börjesson, Fredrik. "New Algorithms for Evaluating Equity Analysts’ Estimates and Recommendations." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-169670.
Full textAbstract:
The purpose of this study is to find improved algorithms to evaluate the work of equity analysts. Initially the study describes how equity analysts work with forecasting earnings per share, and issuing recommendations on whether to invest in stocks. It then goes on to discuss techniques and evaluation algorithms used for evaluating estimates and recommendations found in financial literature. These algorithms are then compared to existing methods in use in the equity research industry. Weaknesses in the existing methods are discussed and new algorithms are proposed. For the evaluation of estimates the main difficulties are concerned with adjusting for the reducing uncertainty over time as new information becomes available, and the problem of identifying which analysts are leading as opposed to herding. For the evaluation of recommendations, the difficulties lie mainly in how to risk-adjust portfolio returns, and how to differentiate between stock-picking ability and portfolio effects. The proposed algorithms and the existing algorithms are applied to a database with over 3500 estimates and 7500 recommendations and an example analyst ranking is constructed. The results indicate that the new algorithms are viable improvements on the existing evaluation algorithms and incorporate new information into the evaluation of equity analysts.Syftet med denna studie är att hitta förbättrade algoritmer för att utvärdera aktieanalytikers arbete. I studien beskrivs inledningsvis hur aktieanalytiker arbetar med att ta fram prognoser för vinst per aktie och rekommendationer för att köpa eller sälja aktier. Därefter diskuteras tekniker och algoritmer för att utvärdera analytikers vinstprognoser och rekommendationer som hämtats från finansiell litteratur. Dessa algoritmer jämförs därefter med befintliga utvärderingsmetoder som används inom aktieanalys-branschen. Svagheter i de befintliga utvärderingsmetoderna diskuteras och nya algoritmer föreslås. För utvärderingen av vinstprognoser diskuteras svårigheterna i att justera för minskande osäkerhet allteftersom ny information blir tillgänglig, samt svårigheter att identifiera vilka analytiker som är ledande och vilka som är efterföljande. För utvärderingen av rekommendationer ligger svårigheterna främst i risk-justering av avkastningar, samt i att skilja mellan förmåga att bedöma enskilda aktiers utveckling och portföljeffekter. De föreslagna algoritmerna och de befintliga algoritmerna tillämpas på en databas med över 3500 vinstestimat och 7500 rekommendationer och ett exempel på ranking av analytiker tas fram. Resultaten indikerar att de nya algoritmerna utgör förbättringar av de befintliga utvärderingsalgoritmerna och integrerar ny information i utvärderingen av aktieanalytiker.
23
Nilsson, Gustav, and William Takolander. "Smarta rekommendationer : Rekommendationer på webbsidor framtagna av maskininlärning." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-39345.
Full textAbstract:
I dagens samhälle är maskininlärning en metod som blir allt mer populär för att lösa olika problem som företag ställs inför världen runt. Många företag har berg av lagrad data som inte används till någon nytta. Den datan kan användas på många olika sätt för att göra förbättringar inom företagen. Ett av sätten är maskininlärning, det har blivit mer och mer populärt för att skapa rekommendationer. Det här projektets syfte är att skapa ett bevis på konceptet att en maskininlärningsmodell är kapabel att ge rekommendationer baserat på historisk data. Projektet kommer vara riktlinjer för hur Centrala Studiestödsnämnden (CSN) ska fortsätta med maskininlärning som ett alternativ till manuella rekommendationer. Det uppnås genom att determinera vilken data som ska användas, förstå datan som används och välja en algoritm som passar den datan. Sedan kan algoritmerna användas för att skapa maskininlärda modeller som kan testas i diverse olika sätt för att se vilken som passar ändamålet. Två modeller skapas med olika algoritmer som båda passar uppgiften. Modellerna testas genom praktiska och teoretiska test. Resultatet visar att algoritmerna är liknande i deras predikterade rekommendationer men har en del variation.In today's society machine learning is a growing method to solve certain problems faced by companies worldwide. Many companies have mountains of stored data that are not being utilised. This data can be used in numerous ways to make improvements within these companies. One of the ways is machine learning, it is used more and more these days to generate recommendations. This project's purpose is to make a proof of concept of a machine learning model capable of giving recommendations based on historical data. This proof of concept will serve as guidelines to Centrala Studiestödsnämnden (CSN) in how they should approach machine learning as an alternative to manual recommendations. This is achieved by determining what data is to be used, understanding the data selected and then picking an algorithm suitable for that data. Then the algorithms will be used to create machine learned models which will be tested in various ways to see which works best for the task at hand. Two models are created with different algorithms that both fit the purpose. The models are tested through practical and theoretical tests. The results show that the algorithms are similar in which predicted recommendations they give but have slight variation.
24
Zhang, Richong. "Probabilistic Approaches to Consumer-generated Review Recommendation." Thèse, Université d'Ottawa / University of Ottawa, 2011. http://hdl.handle.net/10393/19935.
Full textAbstract:
Consumer-generated reviews play an important role in online purchase decisions for many consumers. However, the quality and helpfulness of online reviews varies significantly. In addition, the helpfulness of different consumer-generated reviews is not disclosed to consumers unless they carefully analyze the overwhelming number of available contents. Therefore, it is of vital importance to develop predictive models that can evaluate online product reviews efficiently and then display the most useful reviews to consumers, in order to assist them in making purchase decisions.
This thesis examines the problem of building computational models for predicting whether a consumer-generated review is helpful based on consumers' online votes on other reviews (where a consumer's vote on a review is either HELPFUL or UNHELPFUL), with the aim of suggesting the most suitable products and vendors to consumers.In particular, we propose in this thesis three different helpfulness prediction approaches for consumer-generated reviews. Our entropy-based approach is relatively simple and suitable for applications requiring simple recommendation engine with fully-voted reviews. However, our entropy-based approach, as well as the existing approaches, lack a general framework and are all limited to utilizing fully-voted reviews. We therefore present a probabilistic helpfulness prediction framework to overcome these limitations. To demonstrate the versatility and flexibility of this framework, we propose an EM-based model and a logistic regression-based model. We show that the EM-based model can utilize reviews voted by a very small number of voters as the training set, and the logistic regression-based model is suitable for real-time helpfulness predicting of consumer-generated reviews. To our best knowledge, this is the first framework for modeling review helpfulness and measuring the goodness of models. Although this thesis primarily considers the problem of review helpfulness prediction, the presented probabilistic methodologies are, in general, applicable for developing recommender systems that make recommendation based on other forms of user-generated contents.
25
Gonard, François. "Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS121/document.
Full textAbstract:
La quantité d'informations, de produits et de relations potentielles dans les réseaux sociaux a rendu indispensable la mise à disposition de recommandations personnalisées. L'activité d'un utilisateur est enregistrée et utilisée par des systèmes de recommandation pour apprendre ses centres d'intérêt. Les recommandations sont également utiles lorsqu'estimer la pertinence d'un objet est complexe et repose sur l'expérience. L'apprentissage automatique offre d'excellents moyens de simuler l'expérience par l'emploi de grandes quantités de données.Cette thèse examine le démarrage à froid en recommandation, situation dans laquelle soit un tout nouvel utilisateur désire des recommandations, soit un tout nouvel objet est proposé à la recommandation. En l'absence de données d'intéraction, les recommandations reposent sur des descriptions externes. Deux problèmes de recommandation de ce type sont étudiés ici, pour lesquels des systèmes de recommandation spécialisés pour le démarrage à froid sont présentés.En optimisation, il est possible d'aborder le choix d'algorithme dans un portfolio d'algorithmes comme un problème de recommandation. Notre première contribution concerne un système à deux composants, un sélecteur et un ordonnanceur d'algorithmes, qui vise à réduire le coût de l'optimisation d'une nouvelle instance d'optimisation tout en limitant le risque d'un échec de l'optimisation. Les deux composants sont entrainés sur les données du passé afin de simuler l'expérience, et sont alternativement optimisés afin de les faire coopérer. Ce système a remporté l'Open Algorithm Selection Challenge 2017.L'appariement automatique de chercheurs d'emploi et d'offres est un problème de recommandation très suivi par les plateformes de recrutement en ligne. Une seconde contribution concerne le développement de techniques spécifiques pour la modélisation du langage naturel et leur combinaison avec des techniques de recommandation classiques afin de tirer profit à la fois des intéractions passées des utilisateurs et des descriptions textuelles des annonces. Le problème d'appariement d'offres et de chercheurs d'emploi est étudié à travers le prisme du langage naturel et de la recommandation sur deux jeux de données tirés de contextes réels. Une discussion sur la pertinence des différents systèmes de recommandations pour des applications similaires est proposéeThe need for personalized recommendations is motivated by the overabundance of online information, products, social connections. This typically tackled by recommender systems (RS) that learn users interests from past recorded activities. Another context where recommendation is desirable is when estimating the relevance of an item requires complex reasoning based on experience. Machine learning techniques are good candidates to simulate experience with large amounts of data.The present thesis focuses on the cold-start context in recommendation, i.e. the situation where either a new user desires recommendations or a brand-new item is to be recommended. Since no past interaction is available, RSs have to base their reasoning on side descriptions to form recommendations. Two of such recommendation problems are investigated in this work. Recommender systems designed for the cold-start context are designed.The problem of choosing an optimization algorithm in a portfolio can be cast as a recommendation problem. We propose a two components system combining a per-instance algorithm selector and a sequential scheduler to reduce the optimization cost of a brand-new problem instance and mitigate the risk of optimization failure. Both components are trained with past data to simulate experience, and alternatively optimized to enforce their cooperation. The final system won the Open Algorithm Challenge 2017.Automatic job-applicant matching (JAM) has recently received considerable attention in the recommendation community for applications in online recruitment platforms. We develop specific natural language (NL) modeling techniques and combine them with standard recommendation procedures to leverage past user interactions and the textual descriptions of job positions. The NL and recommendation aspects of the JAM problem are studied on two real-world datasets. The appropriateness of various RSs on applications similar to the JAM problem are discussed
26
Tetler, William G. (William Gore). "A collaborative filtering prediction algorithm for ClassRank subject recommendations." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/46521.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2008.Includes bibliographical references (p. 51).
Undergraduate students at M.I.T. typically utilize three resources when selecting subjects: course specific evaluations, faculty advisors, and peers. While these resources have distinct advantages, they are all limited in scope. The ClassRank web application has been developed to bridge the gap between these resources by providing a simple institute-wide system for undergraduate students to evaluate and rate subjects. The application also provides a solid platform to build new tools utilizing subject evaluation data. To extend the initial core functionality of the ClassRank system, a rating-based subject recommendation algorithm was added to offer students an unbiased perspective on potential subjects of interest. Developed as a Ruby on Rails plugin and then integrated into ClassRank, the recommendation algorithm analyzes subject ratings and provides personalized suggestions to students about subjects that would likely fit their interests and educational goals. The ClassRank web application and recommendation algorithm will provide the M.I.T. undergraduate student body with a unique and invaluable resource for subject selection.
by William G. Tetler.
M.Eng.
27
Ozturk, Gizem. "A Hybrid Veideo Recommendation System Based On A Graph Based Algorithm." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612624/index.pdf.
Full textAbstract:
This thesis proposes the design, development and evaluation of a hybrid video
recommendation system. The proposed hybrid video recommendation system is based
on a graph algorithm called Adsorption. Adsorption is a collaborative filtering algorithm
in which relations between users are used to make recommendations. Adsorption is used
to generate the base recommendation list. In order to overcome the problems that occur
in pure collaborative system, content based filtering is injected. Content based filtering
uses the idea of suggesting similar items that matches user preferences. In order to use
content based filtering, first, the base recommendation list is updated by removing weak
recommendations. Following this, item similarities of the remaining list are calculated
and new items are inserted to form the final recommendations. Thus, collaborative
recommendations are empowered considering item similarities. Therefore, the
developed hybrid system combines both collaborative and content based approaches to
produce more effective suggestions.
28
Ingemarsson, Gabriel, and Rickard Kodet. "Constant ratio optimization in dual-algorithm naive approach to recommendation systems." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208523.
Full textAbstract:
Recommendation systems, i.e. systems that based on some kind of input data produce recommendations for users, are key components of content discovery in today’s information-rich environment. There are many kinds of such recommendation systems, most notably content based and collaborative filtering, and research shows that hybrid approaches which combine multiple techniques perform best. This study examines whether a very simple naive approach hybrid system using a constant weighted average of collaborative and content filtering would benefit from a hybrid approach. One algorithm from each category is run separately and the results are combined by a weighted average, with predetermined ratio for the algorithms. This is done by determining the optimal ratio in two different scenarios. More specifically, this study examines both the general case and sparse data case. This was determined by running two test cases, the general case and sparse data case, with different ratio. The results show that in the general case a constant ratio of 62% collaborative filtering performs best in the general case, while 70% collaborative performs best with sparse data. As such, it is determined that a constant ratio naive approach does improve performance over both content and collaborative filtering alone and therefore benefit from a hybrid approach.Rekommendationssystem, dvs system som baserat på någon typ av indata producerar rekommendationer för användare, är nyckelkomponenter inom system för att upptäcka innehåll i dagens informationsrika samhälle. Det finns många typer av rekommendationssystem, i synnerhet content filtering och collaborative filtering, och forskning visar att hybridsystem som kombinerar flera tekniker fungerar bäst. Denna studie undersöker huruvida naiv implementation av ett hybridsystem som använder ett konstant viktat medelvärde av content filtering och collaborative filtering kan dra nytta av fördelarna med hybridsystem. En algoritm från vardera kategori körs separat och resultatet kombineras med hjälp av ett viktat medelvärde. Vikterna är förutbestämda och är således konstanta. Detta undersöks genom att ta fram de optimala vikterna i två scenarion. Mer specifikt undersöks både det generella fallet och gles data. Detta gjordes genom att köra två testfall, ett för varje scenario, med olika vikter. Resultaten visar sammanfattningsvis att en viktning som ger 62% collaborative filtering presterar bäst i det generella fallet samtidigt som 70% collaborative presterar bäst med gles data. Därmed bedöms det att en naiv strategi med konstant ratio kan förbättra rekommendationerna jämfört med att endast använda collaborative filtering. Därmed bedöms det att ett sådant system kan dra nytta av fördelarna med hybridsystem.
29
Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.
Full text
30
Agosto, Franco Layda J. "Optimisation d'un réseau social d'échange d'information par recommendation de mise en relation." Chambéry, 2005. http://www.theses.fr/2005CHAMS051.
Full textAbstract:
Nous avons observé que sur le Web les internautes ont des besoins changeants d'information. Grâce aux théories des analyses sociales et grâce aux expériences de systèmes de recommandation existants, nous savons que la plupart du temps, ces besoins informationnels sont généralement satisfaits par le fait de " demander à un copain ", c'est à dire, à une connaissance ou à un référent sur le sujet d'intérêt. Par ailleurs, nous avons fait le constat, comme d'autres avant nous, que dans des systèmes d'échanges d'information (e. X. Les groupes d'intérêt), seule une minorité de producteurs d'information est très active, alors qu'une majorité de consommateurs est silencieuse. Pouvons-nous vraiment modifier cette forte tendance? Tenter de répondre à cette question a été au cœur de notre recherche. Pour arriver à répondre positivement, nous avons imaginé la possibilité d'influencer la motivation des personnes à échanger des informations en construisant des mécanismes de régulation dédiés qui intègrent une dynamique d'échanges d'information, de gestion d'information personnelle (favoris) et de conscience sociale. Nous avons donc proposé et mis en œuvre des algorithmes de recommandation utilisant la structure de la topologie du réseau de relation de personnes formée selon leurs échanges et selon les informations qu'elles gèrent. Nous avons développé notre système SoMeONe sous forme d'un service Web. L'apport le plus important de notre approche est, semble t'il, notre idée de recommander des contacts plutôt que de l'information. Pour cela nous nous sommes fortement intéressés à valider l'efficacité de flux d'information dans le réseau social proposé à travers la construction de mesures de qualité de la topologie du réseau. Nous avons donc établi une série de postulats, de principes et d'hypothèses à valider dans notre cadre théorique. Nos hypothèses tiennent compte des objectifs des utilisateurs (obtenir de l'information) et pour cela nous avons intégré des critères de qualité à optimiser pour tenir compte également des objectifs du système (optimiser la structure d'un réseau social). Le moyen pour les atteindre a été d'utiliser des indicateurs sociaux. Ils constituent nos algorithmes que nous nommons SocialRankToday, the World Wide Web is getting essential while surfing for information. Surfers have different information needs. Thanks to results from social network analysis and many other experiences observed from existing recommender systems, we have concluded that a tendency is to prefer information having certain approval : "asking to a friend" means to point out the person having a good level of knowledge about the information needed. As others before us, we have also verified that in many information exchange systems (as mailing groups) only few people produce actively information but a lot of them take it. Can we really modify this strong tendency? Try to answer to this question is the principal objective of our work. To do it in a positive way, we have imagined a way to influence user's motivation to exchange information. For that, we use regulation mechanisms that are intended to promote a dynamic information exchange, to allow users to control their personal information (thanks to bookmarks) and to exhibit a social awareness. This is why we have proposed some recommender algorithms. They exploit the network topology formed of relations between persons exchanging information and the information that they handle. Our approach is then supported by a collaborative web system named SoMeONe (Social Media using Opinions through a trust Network). We think that the most important contribution is our idea of recommending contacts instead of information. For that, we want to validate the efficiency of information flow in the social exchange network. We have then proposed some postulates, principles and hypothesis to validate our approach. The hypothesis take in count the users' objectives (information needed) , and for that some quality criterias have been developed in order to also validate the system's objectives (optimize the social network structure). To raise those objectives we have introduced some social indicators (which are our algorithms) that we named SocialRank
31
Ben, Qingyan. "Flight Sorting Algorithm Based on Users’ Behaviour." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294132.
Full textAbstract:
The model predicts the best flight order and recommend best flight to users. The thesis could be divided into the following three parts: Feature choosing, data-preprocessing, and various algorithms experiment. For feature choosing, besides the original information of flight itself, we add the user’s selection status into our model, which the flight class is, together with children or not. In the data preprocessing stage, data cleaning is used to process incomplete and repeated data. Then a normalization method removes the noise in the data. After various balancing processing, the class-imbalance data is corrected best with SMOTE method. Based on our existing data, I choose the classification model and Sequential ranking algorithm. Use price, direct flight or not, travel time, etc. as features, and click or not as label. The classification algorithms I used includes Logistic Regression, Gradient Boosting, KNN, Decision Tree, Random Forest, Gaussian Process Classifier, Gaussian NB Bayesian and Quadratic Discriminant Analysis. In addition, we also adopted Sequential ranking algorithm. The results show that Random Forest-SMOTE performs best with AUC of ROC=0.94, accuracy=0.8998.Modellen förutsäger den bästa flygordern och rekommenderar bästa flyg till användarna. Avhandlingen kan delas in i följande tre delar: Funktionsval, databehandling och olika algoritms experiment. För funktionsval, förutom den ursprungliga informationen om själva flygningen, lägger vi till användarens urvalsstatus i vår modell, vilken flygklassen är , tillsammans med barn eller inte. Datarengöring används för att hantera dubbletter och ofullständiga data. Därefter tar en normaliserings metod bort bruset i data. Efter olika balanserings behandlingar är SMOTE-metoden mest lämplig för att korrigera klassobalans flyg data. Baserat på våra befintliga data väljer jag klassificerings modell och sekventiell ranknings algoritm. Använd pris, direktflyg eller inte, restid etc. som funktioner, och klicka eller inte som etikett. Klassificerings algoritmerna som jag använde inkluderar Logistic Regression, Gradient Boost, KNN, Decision Tree, Random Forest, Gaussian Process Classifier, Gaussian NB Bayesian and Quadratic Discriminant Analysis. Dessutom antog vi också Sequential ranking algoritm. Resultaten visar att Random Forest-SMOTE presterar bäst med AUC för ROC = 0.94, noggrannhet = 0.8998.
32
Garcia, Ivette Cristina Araujo, Eduardo Rodrigo Linares Salmon, Rosario Villalta Riega, and Alfredo Barrientos Padilla. "Implementation and customization of a smart mirror through a facial recognition authentication and a personalized news recommendation algorithm." Institute of Electrical and Electronics Engineers Inc, 2018. http://hdl.handle.net/10757/624657.
Full textAbstract:
El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.In recent years the advancement of technologies of information and communication (technology ICTs) have helped to improve the quality of people's lives. The paradigm of internet of things (IoT, Internet of things) presents innovative solutions that are changing the style of life of the people. Because of this proposes the implementation of a smart mirror as part of a system of home automation, with which we intend to optimize the time of people as they prepare to start their day. This device is constructed from a reflective glass, LCD monitor, a Raspberry Pi 3, a camera and a platform IoT oriented cloud computing, where the information is obtained to show in the mirror, through the consumption of web services. The information is customizable thanks to a mobile application, which in turn allows the user photos to access the mirror, using authentication with facial recognition and user information to predict the news to show according to your profile. In addition, as part of the idea of providing the user a personalized experience, the Smart Mirror incorporates a news recommendation algorithm, implemented using a predictive model, which uses the algorithm, naive bayes.
Revisión por pares
33
Bodeček, Miroslav. "Algoritmus pro cílené doporučování produktů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-412860.
Full textAbstract:
The goal of this project is to explore the problem of product recommendations in the area of e-commerce and to evaluate known techniques, design product recommendation system for an existing e-commerce site, implement it and test it. This report introduces the problem, briefly examines current state of affairs in this area and defines requirements for a product recommendation module. The concept of data mining in general is introduced. The report proceeds to present detailed design corresponding to defined requirements and summarizes data gathered during testing phase. It concludes with evaluation and with discussion of the remaining goals for this thesis.
34
Karlsson, Sofia. "Purchase behaviour analysis in the retail industry using Generalized Linear Models." Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234684.
Full textAbstract:
This master thesis uses applied mathematicalstatistics to analyse purchase behaviour based on customer data of the Swedishbrand Indiska. The aim of the study is to build a model that can helppredicting the sales quantities of different product classes and identify whichfactors are the most significant in the different models and furthermore, tocreate an algorithm that can provide suggested product combinations in thepurchasing process. Generalized linear models with a Negative binomial distributionare applied to retrieve the predicted sales quantity. Moreover, conditionalprobability is used in the algorithm which results in a product recommendationengine based on the calculated conditional probability that the suggestedcombinations are purchased.From the findings, it can be concluded that all variables considered in themodels; original price, purchase month, colour, cluster, purchase country andchannel are significant for the predicted outcome of the sales quantity foreach product class. Furthermore, by using conditional probability andhistorical sales data, an algorithm can be constructed which createsrecommendations of product combinations of either one or two products that canbe bought together with an initial product that a customer shows interest in.Matematisk statistik tillämpas i denna masteruppsats för att analysera köpbeteende baserat på kunddata från det svenska varumärket Indiska. Syftet med studien är att bygga modeller som kan hjälpa till att förutsäga försäljningskvantiteter för olika produktklasser och identifiera vilka faktorer som är mest signifikanta i de olika modellerna och därtill att skapa en algoritm som ger förslag på rekommenderade produktkombinationer i köpprocessen. Generaliserade linjära modeller med en negativ binomialfördelning utvecklades för att beräkna den förutspådda försäljningskvantiteten för de olika produktklasserna. Dessutom används betingad sannolikhet i algoritmen som resulterar i en produktrekommendationsmotor som baseras på den betingade sannolikheten att de föreslagna produktkombinationerna är inköpta.Från resultaten kan slutsatsen dras att alla variabler som beaktas i modellerna; originalpris, inköpsmånad, produktfärg, kluster, inköpsland och kanal är signifikanta för det predikterade resultatet av försäljningskvantiteten för varje produktklass. Vidare är det möjligt att, med hjälp av betingad sannolikhet och historisk försäljningsdata, konstruera en algoritm som skapar rekommendationer av produktkombinationer av en eller två produkter som kan köpas tillsammans med en produkt som en kund visar intresse för.
35
Priya, Rashmi. "RETAIL DATA ANALYTICS USING GRAPH DATABASE." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/67.
Full textAbstract:
Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions.
Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our goal is to find tools and applications that can be used by retailers to quickly understand their data and take better business decisions. Due to the amount and complexity of data, it is not possible to do such activities manually. We witness that trends change very quickly and retailers want to be quick in adapting the change and taking actions. This needs automation of processes and using algorithms that are efficient and fast. In our work, we mine transaction data by modeling the data as graphs. We use clustering algorithms to discover communities (clusters) in the data and then use the clusters for building a recommendation system that can recommend products to customers based on their buying behavior.
36
Ozbal, Gozde. "A Content Boosted Collaborative Filtering Approach For Movie Recommendation Based On Local &." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610984/index.pdf.
Full textAbstract:
Recently, it has become more and more difficult for the existing web based systems
to locate or retrieve any kind of relevant information, due to the rapid growth of the
World Wide Web (WWW) in terms of the information space and the amount of the
users in that space. However, in today's world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommendation process is sparse. In another sense, when there exists an inadequate number of items or users in the system, unsuccessful recommendations are produced. Within this thesis work, ReMovender, a web based movie recommendation system, which uses a content boosted collaborative filtering approach, will be presented. ReMovender combines the local/global similarity and missing data prediction v techniques in order to handle the previously mentioned sparseness problem effectively. Besides, by putting the content information of the movies into consideration during the item similarity calculations, the goal of making more successful and realistic predictions is achieved.
37
Paraschakis, Dimitris. "Algorithmic and Ethical Aspects of Recommender Systems in e-Commerce." Licentiate thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-7792.
Full textAbstract:
Recommender systems have become an integral part of virtually every e-commerce application on the web. These systems enable users to quickly discover relevant products, at the same time increasing business value. Over the past decades, recommender systems have been modeled using numerous machine learning techniques. However, the adoptability of these models by commercial applications remains unclear. We assess the receptiveness of the industrial sector to algorithmic contributions of the research community by surveying more than 30 e-commerce platforms, and experimenting with various recommenders on proprietary e-commerce datasets. Another overlooked but important factor that complicates the design and use of recommender systems is their ethical implications. We identify and summarize these issues in our ethical recommendation framework, which also enables users to control sensitive moral aspects of recommendations via the proposed “ethical toolbox”. The feasibility of this tool is supported by the results of our user study. Because of moral implications associated with user profiling, we investigate algorithms capable of generating user-agnostic recommendations. We propose an ensemble learning scheme based on Thompson Sampling bandit policy, which models arms as base recommendation functions. We show how to adapt this algorithm to realistic situations when neither arm availability nor reward stationarity is guaranteed.
38
Yanik, Banu Deniz. "Next Page Prediction With Popularity Based Page Rank, Duration Based Page Rank And Semantic Tagging Approach." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614097/index.pdf.
Full textAbstract:
Using page rank and semantic information are frequently used techniques in next page prediction systems. In our work, we extend the use of Page Rank algorithm for next page prediction with several navigational attributes, which are size of the page, duration of the page visit and duration of transition (two page visits sequentially), frequency of page and transition. In our model, we define popularity of transitions and pages by using duration information, use it in a relation with page size, and visit frequency factors. By using the popularity value of pages, we bias conventional Page Rank algorithm and model a next page prediction system that produces page recommendations under given top-n value. Moreover, we extract semantic terms from web URLs in order to tag pages semantically. The extracted terms are mapped into web URLs with different level of details in order to find semantically similar pages for next page recommendations. With this tagging, we model another next page prediction method, which uses Semantic Tagging (ST) similarity and exploits PPR values as a supportive method. Moreover, we model a Hybrid Page Rank (HPR) algorithm that uses both Semantic Tagging based approach and Popularity Based Page Rank values of pages together in order to investigate the effect of PPR and ST with equal weights. In addition, we investigate the effect of local (a synopsis of directed web graph) and global (whole directed web graph) modeling on next page prediction accuracy.
39
Jaradat, Shatha. "OLLDA: Dynamic and Scalable Topic Modelling for Twitter : AN ONLINE SUPERVISED LATENT DIRICHLET ALLOCATION ALGORITHM." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177535.
Full textAbstract:
Providing high quality of topics inference in today's large and dynamic corpora, such as Twitter, is a challenging task. This is especially challenging taking into account that the content in this environment contains short texts and many abbreviations. This project proposes an improvement of a popular online topics modelling algorithm for Latent Dirichlet Allocation (LDA), by incorporating supervision to make it suitable for Twitter context. This improvement is motivated by the need for a single algorithm that achieves both objectives: analyzing huge amounts of documents, including new documents arriving in a stream, and, at the same time, achieving high quality of topics’ detection in special case environments, such as Twitter. The proposed algorithm is a combination of an online algorithm for LDA and a supervised variant of LDA - labeled LDA. The performance and quality of the proposed algorithm is compared with these two algorithms. The results demonstrate that the proposed algorithm has shown better performance and quality when compared to the supervised variant of LDA, and it achieved better results in terms of quality in comparison to the online algorithm. These improvements make our algorithm an attractive option when applied to dynamic environments, like Twitter. An environment for analyzing and labelling data is designed to prepare the dataset before executing the experiments. Possible application areas for the proposed algorithm are tweets recommendation and trends detection.Tillhandahålla högkvalitativa ämnen slutsats i dagens stora och dynamiska korpusar, såsom Twitter, är en utmanande uppgift. Detta är särskilt utmanande med tanke på att innehållet i den här miljön innehåller korta texter och många förkortningar. Projektet föreslår en förbättring med en populär online ämnen modellering algoritm för Latent Dirichlet Tilldelning (LDA), genom att införliva tillsyn för att göra den lämplig för Twitter sammanhang. Denna förbättring motiveras av behovet av en enda algoritm som uppnår båda målen: analysera stora mängder av dokument, inklusive nya dokument som anländer i en bäck, och samtidigt uppnå hög kvalitet på ämnen "upptäckt i speciella fall miljöer, till exempel som Twitter. Den föreslagna algoritmen är en kombination av en online-algoritm för LDA och en övervakad variant av LDA - Labeled LDA. Prestanda och kvalitet av den föreslagna algoritmen jämförs med dessa två algoritmer. Resultaten visar att den föreslagna algoritmen har visat bättre prestanda och kvalitet i jämförelse med den övervakade varianten av LDA, och det uppnådde bättre resultat i fråga om kvalitet i jämförelse med den online-algoritmen. Dessa förbättringar gör vår algoritm till ett attraktivt alternativ när de tillämpas på dynamiska miljöer, som Twitter. En miljö för att analysera och märkning uppgifter är utformad för att förbereda dataset innan du utför experimenten. Möjliga användningsområden för den föreslagna algoritmen är tweets rekommendation och trender upptäckt.
40
CAMARGO, PRISCILLA R. T. L. "Implantação de um programa de controle de qualidade para sistemas de planejamento de tratamento computadorizados de acordo com o TRS 430." reponame:Repositório Institucional do IPEN, 2006. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11412.
Full textAbstract:
Made available in DSpace on 2014-10-09T12:51:37Z (GMT). No. of bitstreams: 0Made available in DSpace on 2014-10-09T14:10:11Z (GMT). No. of bitstreams: 0
Dissertacao (Mestrado)
IPEN/D
Instituto de Pesquisas Energeticas e Nucleares - IPEN/CNEN-SP
41
Plachkov, Alex. "Soft Data-Augmented Risk Assessment and Automated Course of Action Generation for Maritime Situational Awareness." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35336.
Full textAbstract:
This thesis presents a framework capable of integrating hard (physics-based) and soft (people-generated) data for the purpose of achieving increased situational assessment (SA) and effective course of action (CoA) generation upon risk identification. The proposed methodology is realized through the extension of an existing Risk Management Framework (RMF). In this work, the RMF’s SA capabilities are augmented via the injection of soft data features into its risk modeling; the performance of these capabilities is evaluated via a newly-proposed risk-centric information fusion effectiveness metric. The framework’s CoA generation capabilities are also extended through the inclusion of people-generated data, capturing important subject matter expertise and providing mission-specific requirements. Furthermore, this work introduces a variety of CoA-related performance measures, used to assess the fitness of each individual potential CoA, as well as to quantify the overall chance of mission success improvement brought about by the inclusion of soft data. This conceptualization is validated via experimental analysis performed on a combination of real- world and synthetically-generated maritime scenarios. It is envisioned that the capabilities put forth herein will take part in a greater system, capable of ingesting and seamlessly integrating vast amounts of heterogeneous data, with the intent of providing accurate and timely situational updates, as well as assisting in operational decision making.
42
Camargo, Priscilla Roberta Tavares Leite. "Implantação de um programa de controle de qualidade para sistemas de planejamento de tratamento computadorizados de acordo com o TRS 430." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/85/85131/tde-03052012-133401/.
Full textAbstract:
No presente trabalho serão apresentadas as diretrizes e os testes necessários para a implantação de um programa de controle de qualidade para o Eclipse 7.3.10 da Varian no Hospital das Clínicas da Faculdade de Medicina da USP, de acordo com a mais recente publicação da AIEA o TRS 430. Os testes recomendados pelo TRS 430 são basicamente divididos em testes de aceitação, comissionamento (testes dosimétricos e não dosimétricos), e testes rotineiros. O documento da AIEA está sendo implementado para o Eclipse no HC para os feixes de fótons de dois aceleradores lineares da Varian, Clinac 600C e Clinac 2100C. Os testes de aceitação verificaram parâmetros de \"hardware\"; integração do sistema \"network\"; transferência de dados, e \"softwares\". Os resultados obtidos mostraram boa concordância com as especificações do fabricante. Para os testes dosimétricos de comissionamento, foram realizadas medidas de dose absoluta para diversos arranjos experimentais. Esses valores foram comparados com os valores de dose gerados pelo SPTC. A grande maioria dos testes apresentou cerca de 90% a 80% dos pontos comparados, dentro dos níveis de tolerância, ou seja, uma boa concordância entre os valores experimentais e os valores gerados pelo SPTC. Somente arranjos de campos assimétricos apresentaram discordâncias grosseiras, mostrando a necessidade de uma investigação mais apurada para esses casos. Os testes de comissionamento não dosimétricos também apresentaram resultados excelentes, com praticamente todas as ferramentas e desempenho geral do sistema de acordo com as recomendações estipuladas no TRS 430. Foram aplicados também critérios de aceitabilidade para a comparação entre os valores de UMs gerados pelo sistema e os valores de UMs calculados manualmente. Os feixes no Eclipse foram caracterizados com dados transferidos do CadPlan e com dados provenientes do recomissionamento dos aceleradores, assim sendo, para esses testes encontrou- se uma diferença de até 3% para campos conformacionados para os dados de feixe provenientes do recomissionamento dos aceleradores, e de até 4% para os dados de feixe transferidos do CadPlan, sendo que o nível de tolerância estabelecido pelo TRS 430 para o arranjo era de 3%.This work presents the guidelines and necessary tests tom implement a quality assurance program for Eclipse 7.3.10 from Varian at Hospital das Clinicas, Sao Paulo University School of Medicine - Brazil, in accordance with the new IAEA publication TRS 430. The recommended tests for the TRS 430 air mainly classified into acceptance tests, commissioning (dosimetric and non-dosimetric tests), and routine tests. The IAEA document\'s recommendations are being implemented at the hospital for two Varian linear accelerators - Clinac 600C e Clinac 2100C. The acceptance tests verified \'hardware\', integration of network systems, data transfer and \'software\' parameters. The results obtained are in a good agreement with the manufacturer\'s specifications. Measurements of absolute dose in several set-ups were made for the commissioning dosimetric tests. These data were compared to the absolute doses determined by the TPS. The great majority of the tests showed 90% to 80% of the analyzed data in acceptance levels, with a good agreement between the experimental data and the data determined by the TPS. Only settings with asymmetric fields presented significant discords, showing the need for a more detailed inquiry for these settings. The non-dosimetric commissioning tests have also presented excellent results, with virtually all the system tools and general performance in compliance with TRS 430. The acceptance criteria have been applied for a comparison between the values of MUs generated by TPS and the calculated manually ones. The beams have been characterized for Eclipse with data transferred from CadPlan and with data from recommissioning of accelerators, so for these tests it was found a difference of at least 3% for the conformal field shape for the data originated in the beams of recommissioning and at least 4% for the data proceeded from CadPlan. The tolerance level established by TRS 430 for this setting was 3%.
43
Boutet, Antoine. "Decentralizing news personalization systems." Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S023/document.
Full textAbstract:
L'évolution rapide du web a changé la façon dont l'information est créée, distribuée, évaluée et consommée. L'utilisateur est dorénavant mis au centre du web en devenant le générateur de contenu le plus prolifique. Pour évoluer dans le flot d'informations, les utilisateurs ont besoin de filtrer le contenu en fonction de leurs centres d'intérêts. Pour bénéficier de contenus personnalisés, les utilisateurs font appel aux réseaux sociaux ou aux systèmes de recommandations exploitant leurs informations privées. Cependant, ces systèmes posent des problèmes de passage à l'échelle, ne prennent pas en compte la nature dynamique de l'information et soulèvent de multiples questions d'un point de vue de la vie privée. Dans cette thèse, nous exploitons les architectures pair-à-pair pour implémenter des systèmes de recommandations pour la dissémination personnalisée des news. Une approche pair-à-pair permet un passage à l'échelle naturel et évite qu'une entité centrale contrôle tous les profils des utilisateurs. Cependant, l'absence de connaissance globale fait appel à des schémas de filtrage collaboratif qui doivent palier les informations partielles et dynamiques des utilisateurs. De plus, ce schéma de filtrage doit pouvoir respecter la vie privée des utilisateurs. La première contribution de cette thèse démontre la faisabilité d'un système de recommandation de news totalement distribué. Le système proposé maintient dynamiquement un réseau social implicit pour chaque utilisateur basé sur les opinions qu'il exprime à propos des news reçues. Les news sont disséminées au travers d'un protocole épidémique hétérogène qui (1) biaise l'orientation des cibles et (2) amplifie la dissémination de chaque news en fonction du niveau d'intérêt qu'elle suscite. Ensuite, pour améliorer la vie privée des utilisateurs, nous proposons des mécanismes d'offuscation permettant de cacher le profil exact des utilisateurs sans trop dégrader la qualité de la recommandation fournie. Enfin, nous explorons un nouveau modèle tirant parti des avantages des systèmes distribués tout en conservant une architecture centralisée. Cette solution hybride et générique permet de démocratiser les systèmes de recommandations en offrant aux fournisseurs de contenu un système de personnalisation à faible coûtThe rapid evolution of the web has changed the way information is created, distributed, evaluated and consumed. Users are now at the center of the web and becoming the most prolific content generators. To effectively navigate through the stream of available news, users require tools to efficiently filter the content according to their interests. To receive personalized content, users exploit social networks and recommendation systems using their private data. However, these systems face scalability issues, have difficulties in coping with interest dynamics, and raise a multitude of privacy challenges. In this thesis, we exploit peer-to-peer networks to propose a recommendation system to disseminate news in a personalized manner. Peer-to-peer approaches provide highly-scalable systems and are an interesting alternative to Big brother type companies. However, the absence of any global knowledge calls for collaborative filtering schemes that can cope with partial and dynamic interest profiles. Furthermore, the collaborative filtering schemes must not hurt the privacy of users. The first contribution of this thesis conveys the feasibility of a fully decentralized news recommender. The proposed system constructs an implicit social network based on user profiles that express the opinions of users about the news items they receive. News items are disseminated through a heterogeneous gossip protocol that (1) biases the orientation of the dissemination, and (2) amplifies dissemination based on the level of interest in each news item. Then, we propose obfuscation mechanisms to preserve privacy without sacrificing the quality of the recommendation. Finally, we explore a novel scheme leveraging the power of the distribution in a centralized architecture. This hybrid and generic scheme democratizes personalized systems by providing an online, cost-effective and scalable architecture for content providers at a minimal investment cost
44
Naim, Hafida. "Réseaux de service web : construction, analyse et applications." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0083.
Full textAbstract:
Cette thèse se place dans le cadre de services web en dépassant leur description pour considérer leur structuration en réseaux (réseaux d'interaction et réseaux de similitude). Nous proposons des méthodes basées sur les motifs, la modélisation probabiliste et l'analyse des concepts formels, pour améliorer la qualité des services découverts. Trois contributions sont alors proposées: découverte de services diversifiés, recommandation de services et cohérence des communautés de services détectées. Nous structurons d'abord les services sous forme de réseaux. Afin de diversifier les résultats de la découverte, nous proposons une méthode probabiliste qui se base à la fois sur la pertinence, la diversité et la densité des services. Dans le cas de requêtes complexes, nous exploitons le réseau d'interaction de services construit et la notion de diversité dans les graphes pour identifier les services web qui sont susceptibles d'être composables. Nous proposons également un système de recommandation hybride basé sur le contenu et le filtrage collaboratif. L'originalité de la méthode proposée vient de la combinaison des modèles thématiques et les motifs fréquents pour capturer la sémantique commune maximale d'un ensemble de services. Enfin, au lieu de ne traiter que des services individuels, nous considérons aussi un ensemble de services regroupés sous forme de communautés de services pour la recommandation. Nous proposons dans ce contexte, une méthode qui combine la sémantique et la topologie dans les réseaux afin d'évaluer la qualité et la cohérence sémantique des communautés détectées, et classer également les algorithmes de détection de communautésAs a part of this thesis, we exceed the description of web services to consider their structure as networks (i.e. similarity and interaction web service networks). We propose methods based on patterns, topic models and formal concept analysis, to improve the quality of discovered services. Three contributions are then proposed: (1) diversified services discovery, (2) services recommendation and (3) consistency of detected communities. Firstly, we propose modeling the space of web services through networks. To discover the diversified services corresponding to a given query, we propose a probabilistic method to diversify the discovery results based on relevancy, diversity and service density. In case of complex requests, it is necessary to combine multiple web services to fulfill this kind of requests. In this regard, we use the interaction web service network and the diversity notion in graphs to identify all possible services compositions. We also propose a new hybrid recommendation system based on both content and collaborative filtering. Its originality comes from the combination of probabilistic topic models and pattern mining to capture the maximal common semantic of a set of services. Finally, instead of processing individual services, we consider a set of services grouped into service communities for the recommendation. We propose in this context, a new method combining both topology and semantics to evaluate the quality and the semantic consistency of detected communities, and also rank the detection communities algorithms
45
Li, Siying. "Context-aware recommender system for system of information systems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.
Full textAbstract:
Travailler en collaboration n’est plus une question mais une réalité, la question qui se pose aujourd’hui concerne la mise en œuvre de la collaboration de façon à ce qu’elle soit la plus réussie possible. Cependant, une collaboration réussie n’est pas facile et est conditionnée par différents facteurs qui peuvent l’influencer. Il est donc nécessaire de considérer ces facteurs au sein du contexte de collaboration pour favoriser l’efficacité de collaboration. Parmi ces facteurs, le collaborateur est un facteur principal, qui est étroitement associé à l’efficacité et à la réussite des collaborations. Le choix des collaborateurs et/ou la recommandation de ces derniers en tenant compte du contexte de la collaboration peut grandement influencer la réussite de cette dernière. En même temps, grâce au développement des technologies de l’information, de nombreux outils numériques de collaboration sont mis à la disposition tels que les outils de mail et de chat en temps réel. Ces outils numériques peuvent eux-mêmes être intégrés dans un environnement de travail collaboratif basé sur le web. De tels environnements permettent aux utilisateurs de collaborer au-delà de la limite des distances géographiques. Ces derniers laissent ainsi des traces d’activités qu’il devient possible d’exploiter. Cette exploitation sera d’autant plus précise que le contexte sera décrit et donc les traces enregistrées riches en description. Il devient donc intéressant de développer les environnements de travail collaboratif basé sur le web en tenant d’une modélisation du contexte de la collaboration. L’exploitation des traces enregistrés pourra alors prendre la forme de recommandation contextuelle de collaborateurs pouvant renforcer la collaboration. Afin de générer des recommandations de collaborateurs dans des environnements de travail collaboratifs basés sur le web, cette thèse se concentre sur la génération des recommandations contextuelles de collaborateurs en définissant, modélisant et traitant le contexte de collaboration. Pour cela, nous proposons d’abord une définition du contexte de collaboration et choisissons de créer une ontologie du contexte de collaboration compte tenu des avantages de l’approche de modélisation en l’ontologie. Ensuite, une similarité sémantique basée sur l’ontologie est développée et appliquée dans trois algorithmes différents (i.e., PreF1, PoF1 et PoF2) afin de générer des recommandations contextuelles des collaborateurs. Par ailleurs, nous déployons l’ontologie de contexte de collaboration dans des environnements de travail collaboratif basés sur le web en considérant une architecture de système des systèmes d’informations du point de vue des environnements de travail collaboratif basés sur le web. À partir de cette architecture, un prototype correspondant d’environnement de travail collaboratif basé sur le web est alors construit. Enfin, un ensemble de données de collaborations scientifiques est utilisé pour tester et évaluer les performances des trois algorithmes de recommandation contextuelle des collaborateursWorking collaboratively is no longer an issue but a reality, what matters today is how to implement collaboration so that it is as successful as possible. However, successful collaboration is not easy and is conditioned by different factors that can influence it. It is therefore necessary to take these impacting factors into account within the context of collaboration for promoting the effectiveness of collaboration. Among the impacting factors, collaborator is a main one, which is closely associated with the effectiveness and success of collaborations. The selection and/or recommendation of collaborators, taking into account the context of collaboration, can greatly influence the success of collaboration. Meanwhile, thanks to the development of information technology, many collaborative tools are available, such as e-mail and real-time chat tools. These tools can be integrated into a web-based collaborative work environment. Such environments allow users to collaborate beyond the limit of geographical distances. During collaboration, users can utilize multiple integrated tools, perform various activities, and thus leave traces of activities that can be exploited. This exploitation will be more precise when the context of collaboration is described. It is therefore worth developing web-based collaborative work environments with a model of the collaboration context. Processing the recorded traces can then lead to context-aware collaborator recommendations that can reinforce the collaboration. To generate collaborator recommendations in web-based Collaborative Working Environments, this thesis focuses on producing context-aware collaborator recommendations by defining, modeling, and processing the collaboration context. To achieve this, we first propose a definition of the collaboration context and choose to build a collaboration context ontology given the advantages of the ontology-based modeling approach. Next, an ontologybased semantic similarity is developed and applied in three different algorithms (i.e., PreF1, PoF1, and PoF2) to generate context-aware collaborator recommendations. Furthermore, we deploy the collaboration context ontology into web-based Collaborative Working Environments by considering an architecture of System of Information Systems from the viewpoint of web-based Collaborative Working Environments. Based on this architecture, a corresponding prototype of web-based Collaborative Working Environment is then constructed. Finally, a dataset of scientific collaborations is employed to test and evaluate the performances of the three context-aware collaborator recommendation algorithms
46
Garat, Fernanda Velasco. "Presentation Bias in movie recommendation algorithms." Master's thesis, 2021. http://hdl.handle.net/10362/114353.
Full textAbstract:
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization Information Analysis and ManagementThe emergence of video on demand (VOD) has transformed the way the content finds its audience. Several improvements have been made on algorithms to provide better movie recommendations to individuals. Given the huge variety of elements that characterize a film (such as casting, genre, soundtrack, amongst others artistic and technical aspects) and that characterize individuals, most of the improvements relied on accomplishing those characteristics to do a better job regarding matching potential clients to each product. However, little attention has been given to evaluate how the algorithms’ result selection are affected by presentation bias. Understanding bias is key to choosing which algorithms will be used by the companies. The existence of a system with presentation bias and feedback loop is already a problem stated by Netflix. In this sense, this research will fill that gap providing a comparative analysis of the bias of the major movie recommendation algorithms.
47
HUANG, CHIEN-YEH, and 黃建曄. "A Smart Home Recommendation System Based on Optimization Algorithms." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/12966512634938293478.
Full textAbstract:
碩士南臺科技大學
工業管理研究所
105
This research is based on the particle swarm optimization and the SOM-based optimization to make a smart home recommendation system by the internet of things technology. Internet of things (which is short for IOT), connect things, equipment and people at any places and any time through the Internet. Using the information of the environment detected by the sensors, we can exchange information, communication, and sharing, and therefore we could intelligentize the supervision and management. The smart home technology is based on IOT technology, the sensors is going to play an important role in the future. In the more intelligent time, we could communicate with our household electric appliances through the way of M2M(machine to machine). How to find the balance between depending on the electric appliances and the environment protection is the key point that we should discuss. However, a good smart home system not only contain the energy saving but also the comfort. Because different users enter the environment could feel different, so the purpose of this thesis is to consider the users, the electric appliances, energy saving and the comfort in the same time in order to make a good smart home recommendation. So the thesis is based on the SOM-based optimization and C# programing language to make a smart home recommendation that could automatically remember the habit of the environment protection condition in order to increase the convenience and economy of the smart home recommendation. This smart home recommendation consider the environment parameter (the temperature, the humidity, the gas, the ill umination) and the factor of the user (generation, age, job) to give the user the best output to recommend them how to use the equipment. The system contains three kinds of mode: 1.The content-based filtering 2.The collaborative filtering 3. The hybrid approach. The hybrid approach is the best one that could give the user the best recommendation and record it in the system.
48
"Online algorithms for recommendation systems: an experimental study on Amazon's dataset." 2015. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291375.
Full textAbstract:
Zhang, Chi.Thesis M.Phil. Chinese University of Hong Kong 2015.
Includes bibliographical references (leaves 40-41).
Abstracts also in Chinese.
Title from PDF title page (viewed on 27, September, 2016).
49
Lin, Yin-Hung, and 林盈宏. "Open Government Data Recommendation using Clustering Algorithms based on Cloud Computing." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/669unf.
Full textAbstract:
碩士國立虎尾科技大學
資訊工程系碩士班
106
The Big Data Era is coming with the fast-growing data, and the correlation between data is becoming more complicated. If computer knows the data correlation, it can build data linkage. Currently, the open data platform provided by Taiwan government supports query of internal dataset only. This study is going to explore how to link up with the dataset with the data of other platforms, as well as recommend related data. This study proposes Integration K-means and k-NN based on Semantic Cloud Computing Framework (IKKSCCF), and builds the Linked Data Query Platform(LDQP) to validate the feasibility of IKKSCCF. It provides a different method for interface query and combines with the Facebook data to perform correlation analysis between the fans page articles liked by the user on Facebook and the open dataset provided by the government. For the three topics of long-term care, food safety and environmental protection, it screens out the specific vocabulary in the open dataset and the articles by using Jieba suite, so as to count the vocabularies contained in the open dataset and the articles respectively. Moreover, it clusters all datasets through K-means, and then conducts correlation analysis for the classified datasets and the Facebook articles. With k-nearest neighbors algorithm, it finds out the maximum correlation between the article and the open dataset, which is converted into the object needed by Resource Description Framework, and then provided for Semantic Web to achieve semantic inference. When the user logs in Facebook through LDQP, the system will find out the highly-correlated datasets based on the topics concerned by the user on Facebook lately. These are provided for the user to make selection, achieving the individualized recommendation of datasets.
50
Ting, Wu-Chou, and 丁吳稠. "An E-Commerce Recommendation Platform Using Collaborative and Content-based Filtering Algorithms." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/2ynv7z.
Full textAbstract:
碩士國立臺北科技大學
電資碩士班
103
Recommendation system, RS, which be exist in order to dig out hidden valuable information from mega data, is used to improve the making-decision for consumers. With emerging development of Internet and E-Commerce, the quickly exchange among information and surprising growth by unlimited way will result in the appearance of Information Overload. It’s a huge challenge for the e-commerce industry how to seek out business opportunity from Big Data and then assists consumers to find information which is actually need to them. Recommendation system is a kind of service system which offers suggestion item by based on users’ preference, and has been the application of Collaborative Filtering and Content-based recommendation in extensively way. This study will using Collaborative and Content-based Filtering Algorithms and add 2 diversity factors. As this experiment told, the study that mention the method of Collaborative Filtering and Content-based recommendation can reinforce the accuracy and diversity of recommendation item and be the better solution to comparable with other related studying research. Moreover, by classify the consumers’ history to adjust algorithmic method, which fits well in users, can be a great solution to the problem of Collaborative Filtering, with problem on Cold-Start and Scalability, and can cause this system more flexible and modulated.