Ranking and Context-awareness in Recommender Systems

In this thesis we report the results of our research on recommender systems, which addresses some of the critical scientific challenges that still remain open in this domain. Collaborative filtering (CF) is the most common technique of predicting the interests of a user by collecting preference information from many users. In order to determine which items from a collection may be favored by individual users, conventional CF approaches take the ratings previously assigned to items by a target user and use them together with ratings of users with similar preferences to predict the ratings of yet-unseen items. Then, items are recommended in a descending order according to their predicted ratings. While CF has been investigated and improved extensively over the past years, there is still room for substantial improvement. In this thesis we focus on improvement of two critical aspects of CF, namely ranking and context-awareness of the recommendations. In addition, we analyze new developments in the field of collaborative recommendation and elaborate on the challenges related to the evolution of recommender systems and their increasing impact in the future. Based on this analysis, we make recommendations for future research directions in this field