Advanced Factorization Models for Recommender Systems

Recommender Systems have become a crucial tool to serve personalized content and to promote online products and media, but also to recommend restaurants, events, news and dating profiles. The underlying algorithms have a significant impact on the quality of recommendations and have been the subject of many studies in the last two decades. In...

Incorporating Crowd Perspectives into Multimedia Retrieval Systems

The twenty-first century has brought plentiful computational power and bandwidth to the masses and has opened up access to multimedia recording devices for everyone. With these developments, a shift in the landscape of multimedia took place: from traditional one-to-many programming (the paradigm of traditional television) to many-to-many...

Large Scale Image Retrieval for Location Estimation

The geo-graphical location at which an image or video was taken is a key piece of multimedia information. Such geo-information has become an indispensable component of systems enabling personalized and context-aware multimedia services. The research reported in this thesis investigates how to automatically derive geo-information from multimedia...

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...