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Dissertations / Theses on the topic 'CONTEXT-AWARE RECOMMENDER'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Liu, Xiaohu. "Context-aware recommender systems for implicit data." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/13237/.

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Recommender systems are software tools and techniques providing suggestions and recommendations for items to be of use to a user. These sug- gestions can help users make better decisions on choosing products or services, such as which film to watch, what music to listen to or which travel insurance to buy. When making suggestions, many recommender systems do not consider contextual information, such as location or time [5]. Recommender systems that make use of contextual information are called context-aware recommender systems. Many context-aware recommender systems can not generate reliable r

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2

Al-Ghossein, Marie. "Context-aware recommender systems for real-world applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008/document.

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Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont été réalisés dans le domaine durant les deux dernières décennies, menant en particulier à l’exploitation de l’information contextuelle pour modéliser l’aspect dynamique des utilisateurs et des articles. La définition traditionnelle du contexte, adoptée dans la plupart des systèmes de recommandation contextuels, ne répond pas à plusieurs contraintes rencontrées dans les applications du monde réel. Dans cette thèse, nous abord

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3

Al-Ghossein, Marie. "Context-aware recommender systems for real-world applications." Electronic Thesis or Diss., Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008.

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Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont été réalisés dans le domaine durant les deux dernières décennies, menant en particulier à l’exploitation de l’information contextuelle pour modéliser l’aspect dynamique des utilisateurs et des articles. La définition traditionnelle du contexte, adoptée dans la plupart des systèmes de recommandation contextuels, ne répond pas à plusieurs contraintes rencontrées dans les applications du monde réel. Dans cette thèse, nous abord

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4

Li, Siying. "Context-aware recommender system for system of information systems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.

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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 collaborati

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5

Hoque, Tania Tanzin. "A context aware recommender system for tourism with ambient intelligence." Master's thesis, Universidade de Évora, 2020. http://hdl.handle.net/10174/27906.

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Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programmi

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6

Agagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.

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In general, recommender systems attempt to estimate user preference based on historical data. A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts has been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems. Several methods have been used to incorporate contextual information into traditional recommendation algorithms. These methods focus on incorporating contextual information to improve general recommendatio

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7

Strand, Anton, and Markus Gunnarsson. "Code Reviewer Recommendation : A Context-Aware Hybrid Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18288.

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Background. Code reviewing is a commonly used practice in software development. It refers to the process of reviewing new code changes, commonly before they aremerged with the code base. However, in order to perform the review, developers need to be assigned to that task. The problems with a manual assignment includes a time-consuming selection process; limited pool of known candidates; risk of high reuse of the same reviewers (high workload). Objectives. This thesis aims to attempt to address the above issues with a recommendation system. The idea is to receive feedback from experienced devel

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8

Thollot, Raphaël. "Dynamic situation monitoring and Context-Aware BI recommendations." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.

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The amount of information generated and maintained by information systems and their users leads to the increasingly important concern of information overload. Personalized systems have thus emerged to help provide more relevant information and services to the user. In particular, recommender systems appeared in the mid 1990's and have since then generated a growing interest in both industry and academia. Besides, context-aware systems have been developed to model, capture and interpret information about the user's situation, generally in dynamic and heterogeneous environments. Decision support

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9

Alghamdi, Hamzah. "E-Tourism: Context-Aware Points of Interest Finder and Trip Designer." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35676.

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Many countries depend heavily on tourism for their economic growth. The invention of the web has opened new opportunities for tourists to discover new places and live new adventures. However, the number of possible destinations has become huge and even an entire lifespan would not be enough to visit all of these places. Even for one city, there are a significant number of possible places to visit. Nowadays, searching online to find an interesting place to visit is harder than ever, not because there is a lack of information but rather due to the vast amount of information that can be found.

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10

Sauer, Christian Severin. "Knowledge elicitation and formalisation for context and explanation-aware computing with case-based recommender systems." Thesis, University of West London, 2016. http://repository.uwl.ac.uk/id/eprint/2226/.

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Case-based reasoning (CBR), as one of the problem solving paradigms in the field of Artificial Intelligence (AI), is an approach to the re-use of experience to solve problem. The aim of this research was to identify and evaluate existing and new approaches to elicit and formalise knowledge for context-aware systems as well as systems that are able to perform explanation-aware computing. The research was centred on systems that employ the specific AI approach of CBR. The research identified positive and negative effects of knowledge formalisation as well as synergies of knowledge formalisation

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11

Akermi, Imen. "A hybrid model for context-aware proactive recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30101/document.

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L'accès aux informations pertinentes, adaptées aux besoins et au profil de l'utilisateur est un enjeu majeur dans le cadre actuel caractérisé par une prolifération massive des ressources d'information hétérogènes. Le développement d'appareils mobiles équipés de plusieurs fonctionnalités telles que la caméra, le WIFI, la géo-localisation et bien plus d'autres permettent aux systèmes mobiles de recommandation actuels d'être hautement contextualisés et pouvant fournir à l'utilisateur des informations pertinentes au bon moment quand il en a le plus besoin, sans attendre qu'il établisse une interac

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12

Liu, Liwei. "The implication of context and criteria information in recommender systems as applied to the service domain." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/the-implication-of-context-and-criteria-information-in-recommender-systems-as-applied-to-the-service-domain(c3b8e170-8ae0-4e5c-a9b1-508f9c54316a).html.

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Recommender systems support online customers by suggesting products and services of likely interest to them. Research in recommender systems is now starting to recognise the importance of multiple selection criteria and the role of customer context in improving the recommendation output. This thesis investigates the inclusion of criteria and context information in the recommendation process. Firstly, a novel technique for multi-criteria recommendation is proposed. It assumes that some selection criteria for an item (product or a service) will dominate the overall rating, and that these dominan

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13

Hussein, Tim [Verfasser]. "A Conceptual Model and a Software Framework for Developing Context-Aware Hybrid Recommender Systems / Tim Hussein." München : Verlag Dr. Hut, 2013. http://d-nb.info/1042878269/34.

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14

Peker, Serhat. "A Novel User Activity Prediction Model For Context Aware Computing Systems." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613662/index.pdf.

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In the last decade, with the extensive use of mobile electronic and wireless communication devices, there is a growing need for context aware applications and many pervasive computing applications have become integral parts of our daily lives. Context aware recommender systems are one of the popular ones in this area. Such systems surround the users and integrate with the environment<br>hence, they are aware of the users&#039<br>context and use that information to deliver personalized recommendations about everyday tasks. In this manner, predicting user&rsquo<br>s next activity preferences wit

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15

Bouneffouf, Djallel. "DRARS, A Dynamic Risk-Aware Recommender System." Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-01026136.

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L'immense quantité d'information générée et gérée au quotidien par les systèmes d'information et leurs utilisateurs conduit inéluctablement ?a la problématique de surcharge d'information. Dans ce contexte, les systèmes de recommandation traditionnels fournissent des informations pertinentes aux utilisateurs. Néanmoins, avec la propagation récente des dispositifs mobiles (Smartphones et tablettes), nous constatons une migration progressive des utilisateurs vers la manipulation d'environnements pérvasifs. Le problème avec les approches traditionnelles de recommandation est qu'elles n'utilisent p

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16

Marante, Victor, and Simon Månsson. "JMR - Kontextmedveten musikrekommenderare för Spotify." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20024.

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Strömmande musiktjänster erbjuder ett stort utbud av musik. Förutom att användare kan skapa egna listor från detta utbud, erbjuder tjänsterna ofta personliga rekommendationer. Även om rekommendationerna passar användaren väl, passar de inte alltid för situationen de befinner sig i. I denna studie presenteras en artefakt i form av en kontextmedveten musikapplikation som använder Spotify för rekommendation och uppspelning av musik. En kontextmedveten musikapplikation är en applikation som tar användarens kontext i beaktning vid musikrekommendation. I denna studie refererar kontext till den situa

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17

Häger, Alexander. "Contextualizing music recommendations : A collaborative filtering approach using matrix factorization and implicit ratings." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167068.

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Recommender systems are helpful tools employed abundantly in online applications to help users find what they want. This thesis re-purposes a collaborative filtering recommender built for incorporating social media (hash)tags to be used as a context-aware recommender, using time of day and activity as contextual factors. The recommender uses a matrix factorization approach for implicit feedback, in a music streaming setting. Contextual data is collected from users' mobile phones while they are listening to music. It is shown in an offline test that this approach improves recall when compared t

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18

Mustafa, Ghulam. "A methodology for contextual recommendation using artificial neural networks." Thesis, University of Bedfordshire, 2018. http://hdl.handle.net/10547/622833.

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Recommender systems are an advanced form of software applications, more specifically decision-support systems, that efficiently assist the users in finding items of their interest. Recommender systems have been applied to many domains from music to e-commerce, movies to software services delivery and tourism to news by exploiting available information to predict and provide recommendations to end user. The suggestions generated by recommender systems tend to narrow down the list of items which a user may overlook due to the huge variety of similar items or users’ lack of experience in the part

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19

Bahceci, Oktay. "Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210252.

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Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements an

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20

Pereira, Adriano. "AFFECTIVE-RECOMMENDER: UM SISTEMA DE RECOMENDAÇÃO SENSÍVEL AO ESTADO AFETIVO DO USUÁRIO." Universidade Federal de Santa Maria, 2012. http://repositorio.ufsm.br/handle/1/5406.

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Pervasive computing systems aim to improve human-computer interaction, using users situation variables that define context. The boom of Internet makes growing availables items to choose, giving cost in made decision process. Affective Computing has in its goals to identify user s affective/emotional state in a computing interaction, in order to respond to it automatically. Recommendation systems help made decision selecting and suggesting items in scenarios where there are huge information volume, using, traditionally, users prefferences data. This process could be enhanced using context inf

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21

Silva, Fábio Santos da. "PersonalTVware: uma infraestrutura de suporte a sistemas de recomendação sensíveis ao contexto para TV Digital Personalizada." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-31052011-171129/.

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O processo de digitalização da TV em diversos países do mundo tem contribuído para o aumento do volume de programas de TV, o que gera uma sobrecarga de informação. Consequentemente, o usuário está enfrentando dificuldade para encontrar os programas de TV favoritos dentre as várias opções disponíveis. Diante deste cenário, os sistemas de recomendação destacam-se como uma possível solução. Tais sistemas são capazes de filtrar itens relevantes de acordo com as preferências do usuário ou de um grupo de usuários que possuem perfis similares. Entretanto, em diversas recomendações o interesse do usuá

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22

Bambia, Meriam. "Jointly integrating current context and social influence for improving recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30110/document.

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La diversité des contenus recommandation et la variation des contextes des utilisateurs rendent la prédiction en temps réel des préférences des utilisateurs de plus en plus difficile mettre en place. Toutefois, la plupart des approches existantes n'utilisent que le temps et l'emplacement actuels séparément et ignorent d'autres informations contextuelles sur lesquelles dépendent incontestablement les préférences des utilisateurs (par exemple, la météo, l'occasion). En outre, ils ne parviennent pas considérer conjointement ces informations contextuelles avec les interactions sociales entre les u

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23

Abreu, Maria dos Santos de. "Latent Context-aware Recommender Systems." Master's thesis, 2020. https://hdl.handle.net/10216/128481.

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Abreu, Maria dos Santos de. "Latent Context-aware Recommender Systems." Dissertação, 2020. https://hdl.handle.net/10216/128481.

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"Context-Aware Rank-Oriented Recommender Systems." Master's thesis, 2012. http://hdl.handle.net/2286/R.I.15973.

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abstract: Recommender systems are a type of information filtering system that suggests items that may be of interest to a user. Most information retrieval systems have an overwhelmingly large number of entries. Most users would experience information overload if they were forced to explore the full set of results. The goal of recommender systems is to overcome this limitation by predicting how users will value certain items and returning the items that should be of the highest interest to the user. Most recommender systems collect explicit user feedback, such as a rating, and attempt to optimi

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Chang, Yi-Kuo, and 張益國. "A Context-Aware Recommender System for IoT based Interactive Digital Signage." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/62397245555657256041.

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碩士<br>國立臺灣海洋大學<br>運輸科學系<br>104<br>In the online-to-offline (O2O) and omni-channel retailing environment, consumers can switch different channels very easily. In response to the changing of consumer shopping behavior toward M-Commerce, companies strive to develop novel customer interactive IoT systems to engage consumers. IoT-based interactive digital signage has rich multimedia effects, making it a successful marketing tool for many retail firms; they can use the digital signage to provide wayfinding and location information and personal message and advertising to people who stay in front of t

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27

Ho, Cho-Yin, and 何卓穎. "U-tour：A context-aware and ontology based recommender system for Kyoto Travel." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/y5d63w.

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碩士<br>國立彰化師範大學<br>資訊管理學系所<br>104<br>With the development of Internet, more and more travelers search for travel information on the Internet. The travelers could use those information to help them plan itinerary. Because of the universal mobile internet, we could get a variety of information immediately, and it helps us make decision better. There are many recommendation systems using context-aware and user-user similarity model, but these systems did not have an organizational structure. In this study, we propose a travel recommendation system, providing recommendation based on travel topic. T

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Jain, Harshit. "CAPRECIPES: a context-aware personalized recipes recommender for healthy and smart living." Thesis, 2018. https://dspace.library.uvic.ca//handle/1828/9583.

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In the past few years, the general work habits of people have changed dramatically, raising concerns about their well-being. Numerous health-related problems have been observed from their health records such as obesity, diabetes or heart diseases, and unhealthy eating is one of its factors. But these problems can be prevented if people start eating healthy food. The population, in general, is also realizing that healthy eating is important for their well-being. However, they usually resist because they assume that healthy food is not tasty and they do not want to comprise their taste preferenc

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Ou, Ko-Yi, and 歐可翊. "PopFun: A context-aware and ontology-based mobile recommender system for personalized recommendation." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/uwt3rq.

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碩士<br>國立彰化師範大學<br>資訊管理學系數位內容科技與管理碩士班<br>104<br>Currently under development and the growing popularity of mobile devices and the Location Based Service is more popularity issue. If imported a context-aware recommendation system on mobile device which capable of accordance with the user's current location, not only can quickly and easily get your store information, you can quickly find what you want to goods. The context-aware recommendation system is introduces ontology technologies to analyze the relations between the user and the store and the information about the store. The context-aware re

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Guo, Yi-Cheng, and 郭羿呈. "A Context-aware and Social Graph based Restaurant Recommender System for Mobile Devices." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/44672728546709761145.

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碩士<br>國立臺灣大學<br>工程科學及海洋工程學研究所<br>100<br>With the increasing popularity of mobile devices and mobile networks, people can get a soaring amount of information, anywhere, anytime. How to solve the problem of the current information overload and provide personalized recommendation services is an important research topic. This thesis exploits the check-ins of Facebook Open Graph to design a mobile restaurant recommender system, which is based on collaborative filtering. The system summarizes the group preferences from individual users check-in in order to provide group recommendation services. Furt

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DAS, MAYUR. "A CONTEXT-AWARE RECOMMENDER SYSTEM USING THE SENTIMENT ANALYSIS OF TWEETS IN THE ENVIRONMENT OF INTERNET OF THINGS." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15074.

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Currently recommender systems are incorporating context and social information of the user, producing context aware recommender systems. In the future, they will use implicit, local and personal information of the user from the Internet of Things; where anyone and anything will be connected at anytime and anywhere. A Context- Aware Recommendation System has been introduced in this thesis. The fact that the future is for Internet of Things, and the multiple recommendation leads to my system design, in which multi-type rather than one type of recommendations will be recommended to the user

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"The Museum Explorer: User Experience Enhancement In A Museum." Thesis, 2014. http://hdl.handle.net/10388/ETD-2014-12-1899.

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A learner in an informal learning environment, such as a museum, encounters various challenges. After initial assessment, a set of methods were proposed that may enhance a learner’s experience in a museum using computer aided technologies. The most important insight was the need to support the museum visitor in three phases of activity: prior to the visit, during the visit, and after the visit. We hypothesized that software tools that could help connect these three phases would be helpful and valuable supports for the visitor. To test and evaluate our hypothesis, a system called “The Museum E

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