Context-Dependent Items Generation in Collaborative Filtering

Collaborative Filtering (CF) exploits users' recorded ratings for predicting ratings on items not evaluated yet. In classi- cal CF each item is modelled by a set of users' ratings not specifying in which contextual conditions the ratings were obtained (e.g., the time when the item was rated or the goal of the consumption). In some domains the context could heavily inuence the rating values. Therefore, a single rating for each user and item combination could be insucient for making accurate predictions. This paper introduces and an- alyzes a technique, item splitting, for dealing with context by generating new items. In this approach, 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 ctitious items that are used in the prediction algorithm instead of the original one. We eval- uated this approach on real world and semi-synthetic data sets using matrix-factorization and nearest neighbor CF al- gorithms. We also compared our approach to the classical reduction based context-aware CF approach. We show that item splitting can be benecial and its performance depends on the splitting criteria and on the inuence of the contex- tual variables on the item ratings. Moreover, we show that item splitting can perform better than the reduction based approach.