Victor Codina
Francesco Ricci
ABSTRACT
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.
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Index Terms
- Local context modeling with semantic pre-filtering
Recommendations
Distributional semantic pre-filtering in context-aware recommender systems
Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target ...
Semantically-enhanced pre-filtering for context-aware recommender systems
CaRR '13: Proceedings of the 3rd Workshop on Context-awareness in Retrieval and RecommendationSeveral research works have demonstrated that if users' ratings are truly context-dependent, then Context-Aware Recommender Systems can outperform traditional recommenders. In this paper we present a novel contextual pre-filtering approach that exploits ...
ICAMF: Improved Context-Aware Matrix Factorization for Collaborative Filtering
ICTAI '13: Proceedings of the 2013 IEEE 25th International Conference on Tools with Artificial IntelligenceContext-aware recommender system (CARS) can provide more accurate rating predictions and more relevant recommendations by taking into account the contextual in-formation. Yet the state-of-the-art context-aware matrix factorization approaches only ...
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