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Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation - CaRR '13
6HYHUDO UHVHDUFK ZRUNV KDYH GHPRQVWUDWHG WKDW LI XVHUV ¶ UDWLQJV 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 GHWHUPLQLQJ VXFK D VLPLODULW\ ZH UHO\ RQO\ RQ WKH DYDLODEOH XVHUV ¶ ratings and we deem as similar two syntactically different contextual situations that are actually influencing indoi:10.1145/2442670.2442674 fatcat:ofxjnqoocjecpbpjg47r6yn6yu