Content Semantic Similarity Boosted Collaborative Filtering

Collaborative filtering (CF) is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. In practice, a large number of ratings from similar users are not available, due to the sparsity inherent to rating data. Consequently, recommendation quality can be poor. In this paper, we present an effective content semantic similarity boosted CF framework (CSSCF) for combining content meaning and collaboration. Our approach uses a content semantic similarity based rater (CSSR) to enhance existing user data, and then provides personalized suggestions through collaborative filtering. The new model is however more robust to data sparsity, because missing ratings are rated using the CSSR in advance. Experiments demonstrate that the proposed method gives better recommendations than pure collaborative filter.