Experimental evaluation of context-dependent collaborative filtering using item splitting

  title={Experimental evaluation of context-dependent collaborative filtering using item splitting},
  author={Linas Baltrunas and Francesco Ricci},
  journal={User Modeling and User-Adapted Interaction},
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 information is not exploited by standard CF models. This paper introduces and analyzes a… CONTINUE READING
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