Context-based splitting of item ratings in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Matrix factorization techniques for context aware recommendation
Proceedings of the fifth ACM conference on Recommender systems
Context relevance assessment and exploitation in mobile recommender systems
Personal and Ubiquitous Computing
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Context-Aware Recommender Systems locally adapt to a specific contextual situation the rating prediction computed by a traditional context-free recommender. In this paper we present a novel semantic pre-filtering approach that can be tuned to the optimal level of contextualization by aggregating contextual situations that are similar to the target one. The similarities of contextual situations are derived from the available contextually tagged users' ratings according to how similarly the contextual conditions influence the user's rating behavior. We present an extensive evaluation of the performance of our pre-filtering approach on several data sets, showing that it outperforms state-of-the-art context-aware Matrix Factorization approaches.