Complementing Behavioural Modeling with Cognitive Modeling for Better Recommendations

Abstract

Recommender systems are systems that help users in decision-making situations where there is an abundance of choices. We can find them in our everyday lives, for example in online shops. State-of-the-art research in recommender systems has shown the benefits of behavioural modeling. Behavioural modeling means that we use past ratings, purchases, clicks etc. to model the user preferences. However, behavioural modeling is not able to capture certain aspects of the user preferences. In this talk I will show how the usage of complementary research in cognitive models, such as personality and emotions, can benefit recommender systems.