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Identification based recommender systems make no distinction between users and accounts; all the data collected during account sessions are attributed to a single user. In reality this is not necessarily true for all accounts; several different users who have distinct, and possibly very different, preferences may access the same account. Such accounts are identified as multi-user accounts. Strangely, no serious study considering the existence of multi-user accounts in recommender systems has been undertaken. This report quantifies the affect multi-user accounts have on the predictive capabilities of recommender system, focusing on two popular collaborative filtering algorithms, the kNN user-based and item-based models. The results indicate that while the item-based model is largely resistant to multi-user account corruption the quality of predictions generated by the user-based model is significantly degraded. / text

In this study we explore the problem of purchase cart recommendationin the field of retail. How can we push the right customize purchase cart that would consider both habits and serendipity constraints? Recommender Systems application is widely restricted to Internet services providers: movie recommendation, e-commerce, search engine. We brought algorithmic and technological breakthroughs to outdated retail systems while keeping in mind its own specificities: purchase cart rather than single products, restricted interactions between customers and products. After collecting ingenious recommendations methods, we defined two major directions - the correctness and the serendipity - that would serve as discriminant aspects to compare multiple solutions we implemented. We expect our solutions to have beneficial impacts on customers, gaining time and open-mindedness, and gradually obliterate the separation between supermarkets and e-commerce platforms as far as customized experience is concerned.

Conference paper assignment---the task of assigning paper submissions to reviewers---is a key step in the management and smooth functioning of conferences. We study this problem as an application of recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of interest, and an overall distribution of assignments that balances reviewer preferences with conference objectives. Issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of assignments. In this thesis, we present an integrated study of both aspects. First, due to the sparsity of data (relative to other recommender systems applications), we integrate multiple sources of information to learn reviewer/paper preference models, using methods commonly associated with merging content-based and collaborative filtering in the study of large recommender systems. Second, our models are evaluated not just in terms of prediction accuracy, but also in terms of end-assignment quality, and considering multiple evaluation criteria. Using a linear programming-based assignment optimization formulation, we show how our approach better explores the space of potential assignments to maximize the overall affinities of papers assigned to reviewers. Finally, we demonstrate encouraging results on real reviewer preference data gathered during the IEEE ICDM 2007 conference, a premier international data mining conference. Our research demonstrates that there are significant advantages to applying recommender system concepts to the conference paper assignment problem. / Master of Science

Recommender systems have become paramount to customize information access and reduce information overload. They serve multiple uses, ranging from suggesting products and artifacts (to consumers), to bringing people together by the connections induced by (similar) reactions to products and services. This thesis presents a graph-theoretic model that casts recommendation as a process of 'jumping connections' in a graph. In addition to emphasizing the social network aspect, this viewpoint provides a novel evaluation criterion for recommender systems. Algorithms for recommender systems are distinguished not in terms of predicted ratings of services/artifacts, but in terms of the combinations of people and artifacts that they bring together. We present an algorithmic framework drawn from random graph theory and outline an analysis for one particular form of jump called a 'hammock.' Experimental results on two datasets collected over the Internet demonstrate the validity of this approach. / Master of Science

Recommendation is not a new phenomenon arising from the digital era, but an existing social behavior in real life. Recommendation systems facilitate such natural social recommendation behavior and alleviate information overload facing individuals. Among different recommendation techniques proposed in the literature, the collaborative filtering approach is the most successful and widely adopted recommendation technique to date. However, the traditional collaborative filtering recommendation approach ignores proximities between items. That is, all user ratings on items are deemed identically important and given an equal weight in neighborhood formation process. In this study, we proposed a cluster-based collaborative filtering recommendation approach that takes into account the content similarities of items in the collaborative filtering process. Our empirical evaluation results show that the cluster-based collaborative filtering approach improves the prediction accuracy without sacrificing the prediction coverage, using those achieved by the traditional collaborative filtering approach as performance benchmarks. Due to the sparsity problem, when a prediction is made based on few neighbors, the cluster average method could achieve a better prediction accuracy than the proposed approach. Thus, we further proposed an enhanced cluster-based collaborative filtering approach that combines our approach and the cluster average method. The empirical results suggest that the enhanced approach could result in a prediction accuracy comparable to or even better than that accomplished by the cluster average method.

Detaljhandeln i fysiska butiker är utsatt av konkurrens från en betydligt mer innovationsrik e-handel och har därför ett behov av att vidareutvecklas. Ett sätt för detaljhandeln att utvecklas är att utnyttja tekniker som visats fungera bra inom e-handeln. Rekommendationssystem som ger rekommendationer till sina användare har nått stora framgångar och används av i stort sett alla företag inom e-handeln. Den mest använda tekniken för att ta fram rekommendationer kallas för collaborative filtering. Inom detaljhandel används dock inte detta i någon större utsträckning. Det finns därför förhållandevis lite kunskap om vad kunder anser om rekommendationer i butik. Syftet med studien är därför att utvärdera hur ett rekommendationssystem baserat på collaborative filtering presterar i en fysisk butik. Utvärderingen sker genom att mäta träffsäkerheten på rekommendationerna kunder får i en butik samt vad kunderna anser om dessa. Studien ämnar även att ta reda på hur kunder förhåller sig till automatiska rekommendationer i butik. I studien används två forskningsmetodiker för att uppnå dess forskningsmål. Design science har tillämpats för att utvärdera hur ett rekommendationssystem baserat på collaborative filtering presterar i en fysisk butik. En prototyp baserat på collaborative filtering utvecklades för att generera rekommendationer. Prototypen användes sedan i ett användartest som genomfördes i en butiksmiljö. För att belysa hur kunder förhåller sig till automatiska rekommendationer i butik användes en enkätundersökning som utfördes i samband med studiens användartest. Studiens resultat visar att prototypen gav rekommendationer med en hög träffsäkerhet där deltagarna upplevde rekommendationerna som bra och relevanta. Resultaten visar även att deltagarna i studien var positivt inställda till att få rekommendationer i butik. Detta leder till slutsatsen att rekommendationssystem baserat på collaborative filtering kan prestera väl i butiker vilket ger en indikation om att detta kan vara ett sätt för butiker att vidareutveckla handeln. / Retail stores are challenged by competition from the more innovative retailers in e-commerce and thus needs to adapt and evolve in order to stay competitive. This could be accomplished by using technology which has been proven successful in e-commerce. Recommender systems that produces recommendations to its users has been used successfully and is used by essentially all businesses involved in e-commerce. The most common method employed in these recommender systems is called collaborative filtering. Recommender systems have however not yet found its way into retail stores to a greater extent. This has led to a gap in knowledge regarding customer’s opinions of recommendations in retail stores. The purpose of this study is therefore to evaluate how recommender system based on collaborative filtering performs when used in retail stores. The evaluation is performed by measuring the accuracy of the recommendations a customer receives in a retail store as well as what the customer thinks of the recommendation. This study also intends to explore and shed light on people’s opinions concerning automatic recommendations in retail stores. Two different research methods have been used in this study. Design science is being used in order to evaluate how a recommender system based on collaborative filtering performs when used in retail stores. A prototype based on collaborative filtering was developed in order to generate recommendations. The prototype was then used in a user-test taking place in a retail-like environment. In order to shed light on people’s opinions regarding automatic recommendations in retail stores a questionnaire was handed out to the participants in conjunction with the user-test. The results of the study show that the prototype could produce high accuracy recommendations where the participants perceived the recommendations as good and relevant. The results also show that the participants of the study have positive attitude and were in favor of receiving automatic recommendations in retail stores. This leads to the conclusion that recommendations based on collaborative filtering could indeed perform well in retail stores. This indicates that recommender systems using collaborative filtering is one possible way for retail stores to evolve their business.

Personal learning environments (PLEs) aim at putting the learner central stage and comprise a technological approach towards learning tools, services, and artifacts gathered from various usage contexts and to be used by learners. Due to the varying technical skills and competences of PLE users, recommendations appear to be useful for empowering learners to set up their environments so that they can connect to learner networks and collaborate on shared artifacts by using the tools available. In this paper we examine different recommender strategies on their applicability in PLE settings. After reviewing different techniques given by literature and experimenting with our prototypic PLE solution we come to the conclusion to start with an item-based strategy and extend it with model-based and iterative techniques for generating recommendations for PLEs. (author's abstract)

Communities of practices are particular and identified knowledge networks involved in a new global, virtual and digital framework. The study of their specific characteristics, the Legitimate Peripheral Participation and the duality Reification/Participation, provides the necessary background to understand and formalize the barriers and the limits in this new context. <br />In order to overcome these ones, the analysis of the tools and the methods for computerized reification (content analysis, information architecture, information visualization) and for enrichment and assessment of content and users (Human-Computer Interactions, Collaborative filtering) enables to develop a methodology to support the application of an automated and interactive reification process in a virtual Communities of Practices.

The goal of the thesis is to extend the kernel methods to matrix factorization(MF) for collaborative ltering(CF). In current literature, MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. The best known member of kernel methods is support vector machine (SVM) on linearly non-separable data. In this thesis, we apply kernel methods on MF, embedding the data into a possibly higher dimensional space and conduct factorization in that space. To improve kernelized matrix factorization, we apply multi-kernel learning methods to select optimal kernel functions from the candidates and introduce L2-norm regularization on the weight learning process. In our empirical study, we conduct experiments on three real-world datasets. The results suggest that the proposed method can improve the accuracy of the prediction surpassing state-of-art CF methods.