Learning and exploiting context dependencies for robust recommendations

Abstract

We consider the recommendation problem, where a set of available items or choices are rated

and recommended to users accordingly. Over and above the ratings information used in traditional

filtering algorithms, the context of the user-recommender interaction is used to improve

the recommendation quality. Specifically, we study how the effective learning and exploitation

of context dependencies can help to generate more personal and relevant recommendations.