Movie Recommender: Semantically enriched unified relevance model for rating prediction in collaborative filtering

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.