Adaptive Fusion Method For User-Based And Item-Based Collaborative Filtering

In many e-commerce sites, recommender systems, which provide personalized recommendations from among a large number of items, have recently been introduced. Collaborative filtering is one of the most successful algorithms which provide recommendations using ratings of users on items. There are two approaches: user-based and item-based collaborative filtering. Additionally a unifying method for user-based and item-based collaborative filtering was proposed to improve the recommendation accuracy. The unifying approach uses a constant value as a weight parameter to unify both algorithms. However, because the optimal weight for unifying is actually different depending on the situation, the algorithm should estimate an appropriate weight dynamically, and should use it. In this research, we first investigate the relationship between recommendation accuracy and the weight parameter. The results show that the optimal weight is different depending on the situation. Second, we propose an approach for estimation of the appropriate weight value based on collected ratings. Then, we discuss the effectiveness of the proposed approach based on both multi-agent simulation and the MovieLens dataset. The results show that the proposed approach can estimate the weight value within an error rate of 0.5% for the optimal weight.