A Trust-Enriched Approach for Item-Based Collaborative Filtering Recommendations

The item-based collaborative filtering (CF) is one of the most successful approaches utilized by the recommendation systems. The basic concept behind it is to recommend those items to users which are similar to other items that these users have been interested in recently. This paper proposes a hybrid method that integrates user trust relations with item-based CF. This is achieved by incorporating user social similarities into the computation of item similarities. Performance evaluation of the proposed method is done by comparing the results with the traditional item-based CF. The experiment results demonstrate that the proposed approach achieves better accuracy.