User Model User-Adap Inter (2014) 24:7–34 DOI 10.1007/s11257-012-9137-9 ORIGINAL PAPER Experimental evaluation of context-dependent collaborative filtering using item splitting Linas Baltrunas · Francesco Ricci Received: 7 March 2012 / Accepted in revised form: 2 November 2012 / Published online: 7 February 2013 © Springer Science+Business Media Dordrecht 2013 Abstract Collaborative Filtering (CF) computes recommendations by leveraging a historical data set of users’ ratings for items. CF assumes that the users’ recorded ratings can help in predicting their future ratings. This has been validated extensively, but in some domains the user’s ratings can be influenced by contextual conditions, such as the time, or the goal of the item consumption. This type of contextual infor- mation is not exploited by standard CF models. This paper introduces and analyzes a novel technique for context-aware CF called Item Splitting. In this approach items experienced in two alternative contextual conditions are “split” into two items. This means that the ratings of a split item, e.g., a place to visit, are assigned (split) to two new fictitious items representing for instance the place in summer and the same place in winter. This split is performed only if there is statistical evidence that under these two contextual conditions the items ratings are different; for instance, a place may be rated higher in summer than in winter. These two new fictitious items are then used, together with the unaffected items, in the rating prediction algorithm. When the system must predict the rating for that “split” item in a particular contextual condition (e.g., in summer), it will consider the new fictitious item representing the original one in that particular contextual condition, and will predict its rating. 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 method used to determine which items to split. We also show that the benefit of the method is determined by the relevance of the contextual factors that are used to split. L. Baltrunas (B ) Telefonica Research, Plaza de Ernest Lluch i Martin, 5, 08019 Barcelona, Spain e-mail: linas@tid.es F. Ricci Free University of Bozen-Bolzano, Piazza Domenicani, 3, 39100 Bozen-Bolzano, Italy e-mail: fricci@unibz.it 123