Information-seekers use a variety of information stores including electronic systems and the physical world experience of their community. Within electronic systems, information-seekers often report feelings of being lost and suffering from information overload. However, in the physical world they tend not to report the same negative feelings. This work draws on existing research including Collaborative Filtering, Recommender Systems and Social Navigation and reports on a new observational study of information-seeking behaviours. From the combined findings of the research and the observational study, a set of design considerations for the creation of a new electronic interface is proposed. Two new interfaces, the second built from the recommendations of the first, and a supporting methodology are created using the proposed design considerations. The second interface, the Collaborative Index, is shown to allow physical world behaviours to be used in the electronic world and it is argued that this has resulted in an alternative and preferred access route to information. This preferred route is a product of information-seekers' interactions 'within the machine' and maintains the integrity of the source information and navigational structures. The methodology used to support the Collaborative Index provides information managers with an understanding of the information-seekers' needs and an insight into their behaviours. It is argued that the combination of the Collaborative Index and its supporting methodology has provided the capability for information-seekers and information managers to 'enter into the machine', producing benefits for both groups.

The goal of the thesis is first to find available information about user preferences, user feedback and their acquisition, processing, storing etc. The collected information is then used for making suggestions / advices for the creating an recommender system for the web shops (with special emphasis on implicit feedback). The following chapters introduces UPComp - our solution of the recommender system for the web shops. The UPComp is written in the programming language PHP and uses MySQL database. The thesis also includes testing of the UPComp on real-user web shop sites slantour.cz and antikvariat-ichtys.cz.

The aim of this work is to explore recommender systems for prediction user's future film ratings according to their previous ratings. Author will describe available algorithms and compare their results with his own algorithm. The goal is to find algorithm with the highest prediction accuracy and find the most important parameters for a good predictions.

Master of Science / Department of Computing and Information Sciences / Daniel A. Anderson / Most of the e-commerce websites like Amazon, EBay, hotels, trip advisor etc. use recommender systems to recommend products to their users. Some of them use the knowledge of history/ of all users to recommend what kind of products the current user may like (Collaborative filtering) and some use the knowledge of the products which the user is interested in and make recommendations (Content based filtering). An example is Amazon which uses both kinds of techniques.. These recommendation systems can be represented in the form of a graph where the nodes are users and products and edges are between users and products. The aim of this project is to build a recommender system for recipes by using the data from allrecipes.com. Allrecipes.com is a popular website used all throughout the world to post recipes, review them and rate them. To understand the data set one needs to know how the recipes are posted and rated in allrecipes.com, whose details are given in the paper. The network of allrecipes.com consists of users, recipes and ingredients. The aim of this research project is to extensively study about two algorithms adsorption and matrix factorization, which are evaluated on homogeneous networks and try them on the heterogeneous networks and analyze their results. This project also studies another algorithm that is used to propagate influence from one network to another network. To learn from one network and propagate the same information to another network we compute flow (influence of one network on another) as described in [7]. The paper introduces a variant of adsorption that takes the flow values into account and tries to make recommendations in the user-recipe and the user-ingredient networks. The results of this variant are analyzed in depth in this paper.

This report describes a distributed approach to social filtering based on the agent metaphor. Firstly, previous approaches are described, such as cognitive filtering and social filtering. Then a couple of previously implemented systems are presented and then a new system design is proposed. The main goal is to give the requirements and design of an agent-based system that recommends web-documents. The presented approach combines cognitive and social filtering to get the advantages from both techniques. Finally, a prototype implementation called WebCondor is described and results of testing the system are reported and discussed.

Recommender Systems (RS) support users to find relevant content, such as movies, books, songs, and other products based on their preferences. Such preferences are gathered by analyzing past users interactions, however, data collected for this purpose are typically prone to sparsity and high dimensionality. Clustering-based techniques have been proposed to handle these problems effectively and efficiently by segmenting the data into a number of similar groups based on predefined characteristics. Although these techniques have gained increasing attention in the recommender systems community, they are usually bound to a particular recommender system and/or require critical parameters, such as the number of clusters. In this work, we present three variants of a general-purpose method to optimally extract users groups from a hierarchical clustering algorithm specifically targeting RS problems. The proposed extraction methods do not require critical parameters and can be applied prior to any recommendation system. Our experiments have shown promising recommendation results in the context of nine well-known public datasets from different domains. / Sistemas de Recomendação auxiliam usuários a encontrar conteúdo relevante, como filmes, livros, músicas entre outros produtos baseando-se em suas preferências. Tais preferências são obtidas ao analisar interações passadas dos usuários, no entanto, dados coletados com esse propósito tendem a tipicamente possuir alta dimensionalidade e esparsidade. Técnicas baseadas em agrupamento de dados têm sido propostas para lidar com esses problemas de foma eficiente e eficaz ao dividir os dados em grupos similares baseando-se em características pré-definidas. Ainda que essas técnicas tenham recebido atenção crescente na comunidade de sistemas de recomendação, tais técnicas são usualmente atreladas a um algoritmo de recomendação específico e/ou requerem parâmetros críticos, como número de grupos. Neste trabalho, apresentamos três variantes de um método de propósitvo geral de extração ótima de grupos em uma hierarquia, atacando especificamente problemas em Sistemas de Recomendação. Os métodos de extração propostos não requerem parâmetros críticos e podem ser aplicados antes de qualquer sistema de recomendação. Os experimentos mostraram resultados promissores no contexto de nove bases de dados públicas conhecidas em diferentes domínios.

Dans de nombreux domaines, les données peuvent être de grande dimension. Ça pose le problème de la réduction de dimension. Les techniques de réduction de dimension peuvent être classées en fonction de leur but : techniques pour la représentation optimale et techniques pour la classification, ainsi qu'en fonction de leur stratégie : la sélection et l'extraction des caractéristiques. L'ensemble des caractéristiques résultant des méthodes d'extraction est non interprétable. Ainsi, la première problématique scientifique de la thèse est comment extraire des caractéristiques latentes interprétables? La réduction de dimension pour la classification vise à améliorer la puissance de classification du sous-ensemble sélectionné. Nous voyons le développement de la tâche de classification comme la tâche d'identification des facteurs déclencheurs, c'est-à-dire des facteurs qui peuvent influencer le transfert d'éléments de données d'une classe à l'autre. La deuxième problématique scientifique de cette thèse est comment identifier automatiquement ces facteurs déclencheurs? Nous visons à résoudre les deux problématiques scientifiques dans le domaine d'application des systèmes de recommandation. Nous proposons d'interpréter les caractéristiques latentes de systèmes de recommandation basés sur la factorisation de matrices comme des utilisateurs réels. Nous concevons un algorithme d'identification automatique des facteurs déclencheurs basé sur les concepts d'analyse par contraste. Au travers d'expérimentations, nous montrons que les motifs définis peuvent être considérés comme des facteurs déclencheurs / In many application areas, data elements can be high-dimensional. This raises the problem of dimensionality reduction. The dimensionality reduction techniques can be classified based on their aim: dimensionality reduction for optimal data representation and dimensionality reduction for classification, as well as based on the adopted strategy: feature selection and feature extraction. The set of features resulting from feature extraction methods is usually uninterpretable. Thereby, the first scientific problematic of the thesis is how to extract interpretable latent features? The dimensionality reduction for classification aims to enhance the classification power of the selected subset of features. We see the development of the task of classification as the task of trigger factors identification that is identification of those factors that can influence the transfer of data elements from one class to another. The second scientific problematic of this thesis is how to automatically identify these trigger factors? We aim at solving both scientific problematics within the recommender systems application domain. We propose to interpret latent features for the matrix factorization-based recommender systems as real users. We design an algorithm for automatic identification of trigger factors based on the concepts of contrast analysis. Through experimental results, we show that the defined patterns indeed can be considered as trigger factors

Les systèmes de recommandation se sont imposés comme étant des outils indispensables face à une quantité de données qui ne cesse chaque jour de croître depuis l'avènement d'Internet. Leur objectif est de proposer aux utilisateurs des items susceptibles de les intéresser sans que ces derniers n'aient besoin d'agir pour les obtenir. Après s'être majoritairement focalisés sur la précision de la prédiction d'intérêt, ces systèmes ont évolué pour prendre en compte d'autres critères dans leur processus de recommandation, tels que les facteurs humains inhérents à la prise de décision, afin d'améliorer la qualité et l'utilité des recommandations. Cependant, la prise en compte de certains facteurs humains tels que la diversité et le contexte demeure critiquable. Alors que le contexte des utilisateurs est inféré sur la base d'informations collectées à l'insu de leur vie privée, la prise en compte de la diversité est quant à elle réduite à une dimension qu'un système se doit de maximiser. Or, certains travaux récents démontrent que la diversité correspond à un besoin évoluant dynamiquement au cours du temps, et dont la proportion à insuffler dans les recommandations est dépendante de la tâche effectuée (i.e du contexte). Partant du postulat inverse selon lequel l'analyse de l'évolution de la diversité au cours du temps permet de définir le contexte de l'utilisateur, nous proposons dans ce manuscrit une nouvelle approche de modélisation contextuelle basée sur la diversité. En effet, nous soutenons qu'une variation de diversité remarquable peut être la conséquence d'un changement de contexte et qu'il faut alors adapter la stratégie de recommandation en conséquence. Nous présentons la première approche de la littérature permettant de modéliser en temps réel l'évolution de la diversité, ainsi qu'une nouvelle famille de contextes dits implicites n'exploitant aucune donnée sensible. La possibilité de remplacer les contextes traditionnels (explicites) par les contextes implicites est confirmée de plusieurs manières. Premièrement, nous démontrons sur deux corpus issus d'applications réelles qu'il existe un fort recouvrement entre les changements de contextes explicites et les changements de contextes implicites. Deuxièmement, une étude utilisateur impliquant de nombreux participants nous permet de démontrer l'existence de liens entre les contextes explicites et les caractéristiques des items consultés dans ces derniers. Fort de ces constats et du potentiel offert par nos modèles, nous présentons également plusieurs approches de recommandation et de prise en compte des besoins des utilisateurs / Recommender Systems (RS) have become essential tools to deal with an endless increasing amount of data available on the Internet. Their goal is to provide items that may interest users before they have to find them by themselves. After being exclusively focused on the precision of users' interests prediction task, RS had to evolve by taking into account other criteria like human factors involved in the decision-making process while computing recommendations, so as to improve their quality and usefulness of recommendations. Nevertheless, the way some human factors, such as context and diversity needs, are managed remains open to criticism. While context-aware recommendations relies on exploiting data that are collected without any consideration for users' privacy, diversity has been coming down to a dimension which has to be maximized. However recent studies demonstrate that diversity corresponds to a need which evolves dynamically over time. In addition, the optimal amount of diversity to provide in the recommendations depends on the on-going task of users (i.e their contexts). Thereby, we argue that analyzing the evolution of diversity over time would be a promising way to define a user's context, under the condition that context is now defined by item attributes. Indeed, we support the idea that a sudden variation of diversity can reflect a change of user's context which requires to adapt the recommendation strategy. We present in this manuscript the first approach to model the evolution of diversity over time and a new kind of context, called ``implicit contexts'', that are respectful of privacy (in opposition to explicit contexts). We confirm the benefits of implicit contexts compared to explicit contexts from several points of view. As a first step, using two large music streaming datasets we demonstrate that explicit and implicit context changes are highly correlated. As a second step, a user study involving many participants allowed us to demonstrate the links between the explicit contexts and the characteristics of the items consulted in the meantime. Based on these observations and the advantages offered by our models, we also present several approaches to provide privacy-preserving context-aware recommendations and to take into account user's needs

<div>Over the past decade, universities had a significant improvement in using online learning tools. A standard learning management system provides fundamental functionalities to satisfy the basic needs of its users. The new generation of learning management systems have introduced a novel system that provides social networking features. An unprecedented number of users use the social aspects of such platforms to create their profile, collaborate with other users, and find their desired career path. Nowadays there are many learning systems which provide learning materials, certificates, and course management systems. This allows us to utilize such information to help the students and the instructors in their academic life. </div><div><br></div><div>The presented research work's primary goal is to focus on creating an intelligent personal assistant within the social learning systems. The proposed personal assistant has a human-like persona, learns about the users, and recommends useful and meaningful materials for them. The designed system offers a set of features for both institutions and members to achieve their goal within the learning system. It recommends jobs and friends for the users based on their profile. The proposed agent also prioritizes the messages and shows the most important message to the user. </div><div><br></div><div>The developed software supports model-controller-view architecture and provides a set of RESTful APIs which allows the institutions to integrate the proposed intelligent agent with their learning system. <br></div>

"The goal of this thesis is to extend the theory and practice of matrix completion algorithms, and how they can be utilized, improved, and scaled up to handle large data sets. Matrix completion involves predicting missing entries in real-world data matrices using the modeling assumption that the fully observed matrix is low-rank. Low-rank matrices appear across a broad selection of domains, and such a modeling assumption is similar in spirit to Principal Component Analysis. Our focus is on large scale problems, where the matrices have millions of rows and columns. In this thesis we provide new analysis for the convergence rates of matrix completion techniques using convex nuclear norm relaxation. In addition, we validate these results on both synthetic data and data from two real-world domains (recommender systems and Internet tomography). The results we obtain show that with an empirical, data-inspired understanding of various parameters in the algorithm, this matrix completion problem can be solved more efficiently than some previous theory suggests, and therefore can be extended to much larger problems with greater ease. "