Semantically-enhanced pre-filtering for context-aware recommender systems
Document typeConference report
PublisherACM Press. Association for Computing Machinery
Rights accessRestricted access - publisher's policy
Several 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 the implicit semantic similarity of contextual situations. For determining such a similarity we rely only on the available users' ratings and we deem as similar two syntactically different contextual situations that are actually influencing in a similar way the user's rating behavior. We validate the proposed approach using two contextually tagged ratings data sets showing that it outperforms a traditional pre-filtering approach and a state-of-the-art context-aware Matrix Factorization model.
CitationCodina, V.; Ceccaroni, L. Semantically-enhanced pre-filtering for context-aware recommender systems. A: Workshop on Context-aware Retrieval and Recommendation. "Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation". New York: ACM Press. Association for Computing Machinery, 2013, p. 15-18.