Multi-Agent Modeling of Risk-Aware and Privacy-Preserving Recommender Systems

Recent progress in the field of recommender systems has led to increases in the accuracy and significant improvements in the personalization of recommendations. These results are being achieved in general by gathering more user data and generating relevant insights from it. However, user privacy concerns are often underestimated and recommendation risks are not usually addressed. In fact, many users are not sufficiently aware of what data is collected about them and how the data is collected (e.g., whether third parties are collecting and selling their personal information). Research in the area of recommender systems should strive towards not only achieving high accuracy of the generated recommendations but also protecting the user’s privacy and making recommender systems aware of the user’s context, which involves the user’s intentions and the user’s current situation. Through research it has been established that a tradeoff is required between the accuracy, the privacy and the risks in a recommender system and that it is highly unlikely to have recommender systems completely satisfying all the context-aware and privacy-preserving requirements. Nonetheless, a significant attempt can be made to describe a novel modeling approach that supports designing a recommender system encompassing some of these previously mentioned requirements. This thesis focuses on a multi-agent based system model of recommender systems by introducing both privacy and risk-related abstractions into traditional recommender systems and breaking down the system into three different subsystems. Such a description of the system will be able to represent a subset of recommender systems which can be classified as both risk-aware and privacy-preserving. The applicability of the approach is illustrated by a case study involving a job recommender system in which the general design model is instantiated to represent the required domain-specific abstractions

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available