<XML><RECORDS><RECORD><REFERENCE_TYPE>3</REFERENCE_TYPE><REFNUM>9077</REFNUM><AUTHORS><AUTHOR>Moshfeghi,Y.</AUTHOR><AUTHOR>Agarwal,D.</AUTHOR><AUTHOR>Piwowarski,B.</AUTHOR><AUTHOR>Jose,J.M.</AUTHOR></AUTHORS><YEAR>2009</YEAR><TITLE>Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering</TITLE><PLACE_PUBLISHED>ECIR '09: European Conference on Information Retrieval</PLACE_PUBLISHED><PUBLISHER>Springer</PUBLISHER><LABEL>Moshfeghi:2009:9077</LABEL><ABSTRACT>Collaborative recommender systems aim to recommend items to a user based on the information gathered from other users who have similar interests. The current state-of-the-art systems fail to consider the underlying semantics involved when rating an item. This in turn contributes to many false recommendations. These models hinder the possibility of explaining why a user has a particular interest or why a user likes a particular item. In this paper, we develop an approach incor- porating the underlying semantics involved in the rating. Experiments on a movie database show that this improves the accuracy of the model.</ABSTRACT></RECORD></RECORDS></XML>