context-based splitting of item ratings in collaborative filtering
Collaborative Filtering (CF) recommendations are computed by leveraging a historical data set of users' ratings for items. It assumes that the users' previously recorded ratings can help in predicting future ratings. This has been validated extensively, but in some domains item ratings can be influenced by contextual conditions, such as the time or the goal of the item consumption. This type of information is not exploited by standard CF models. This paper introduces and analyzes a novel pre-filtering technique for context-aware CF called item splitting. In this approach, the ratings of certain items are split, according to the value of an item-dependent contextual condition. Each split item generates two fictitious items that are used in the prediction algorithm instead of the original one. We evaluated this approach on real world and semi-synthetic data sets using matrix-factorization and nearest neighbor CF algorithms. We show that item splitting can be beneficial and its performance depends on the item selection method and on the influence of the contextual variables on the item ratings.
collaborative filtering item split context-based reasoning recommender systems