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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering
verfasst von : Yashar Moshfeghi, Deepak Agarwal, Benjamin Piwowarski, Joemon M. Jose
Erschienen in: Advances in Information Retrieval
Verlag: Springer Berlin Heidelberg
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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.