User's activity driven short-term context inference

Abstract

Customer decision making process is not invariant. Actual circumstances have a great influence on user's preference adjustments, therefore an absence of incorporating contextual information leads to sub-optimal prediction performance. A popular approach in recommender systems is to treat a context as a set of identifiable and observable attributes while assuming their full separability from an activity. In contrast, we believe that the context emerges from the activity and its change can be perceived and possibly predicted by using mined patterns of its evolution on multiple levels, starting at individual sessions. This paper presents concepts, ideas and motivation for our PhD research project.