An Item-Based Collaborative Filtering Algorithm Utilizing the Average Rating for Items

Collaborative filtering is one of the most promising implementation of recommender system. It can predict the target user's personalized rating for unvisited items based on his historical observed preference. The majority of various collaborative filtering algorithms emphasizes the personalized factor of recommendation separately, but ignores the user's general opinion about items. The unbalance between personalization and generalization hinders the performance improvement for existing collaborative filtering algorithms. This paper proposes a refined item-based collaborative filtering algorithm utilizing the average rating for items. The proposed algorithm balances personalization and generalization factor in collaborative filtering to improve the overall performance. The experimental result shows an improvement in accuracy in contrast with classic collaborative filtering algorithm.