Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering

Authors:
Yashar Moshfeghi;Deepak Agarwal;Benjamin Piwowarski;Joemon M. Jose
Affiliations:
Department of Computing Science, University of Glasgow, Glasgow, UK;Department of Computing Science, University of Glasgow, Glasgow, UK;Department of Computing Science, University of Glasgow, Glasgow, UK;Department of Computing Science, University of Glasgow, Glasgow, UK
Venue:
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Year:
2009

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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 incorporating the underlying semantics involved in the rating. Experiments on a movie database show that this improves the accuracy of the model.