Context-Dependent Recommendations with Items Splitting

Recommender systems are intelligent applications that help on-line users to tackle information overload by providing recommendations of relevant items. Collaborative Filtering (CF) is a recommendation technique that exploits users’ explicit feedbacks on items to predict the relevance of items not evaluated yet. In classical CF users’ ratings are not specifying in which contextual conditions the item was evaluated (e.g., the time when the item was rated or the goal of the consumption). But, in some domains the context could heavily influence the relevance of the item and this must be taken into account. This paper analyzes the behavior of a technique which deals with context by generating new items that are restricted to a contextual situation. The ratings’ vectors of some items are split in two vectors containing the ratings collected in two alternative contextual conditions. Hence, each split generates two fictitious items that are used in the prediction algorithm instead of the original one. We evaluated this approach on semi-synthetic data sets measuring precision and recall while using a matrix-factorization algorithm for generating rating predictions. We compared our approach to the previously introduced reduction based method. We show that item splitting can improve system accuracy. Moreover, item splitting leads to a better recall than the reduction based approach.