Adaptive Fusion Method for User-Based and Item-Based Collaborative Filtering

In many E-commerce sites, recommender systems, which provide personalized recommendation from among a large number of items, are recently introduced. Collaborative ltering is one of the most successful algorithms which provide recommendations using ratings of users on items. There are two approaches such as user-based and item-based collaborative ltering. Additionally a unifying method for userbased and item-based collaborative ltering 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 di erent by the situation, the algorithm should estimate an appropriate weight dynamically, and should use it. In this research, rst, we investigated the relationship between recommendation accuracy and the weight parameter. The results show the optimal weight is di erent depending on the situation. Second, we propose an approach for estimation of the appropriate weight value based on collected ratings. Then, we discussed the e ectiveness of the proposed approach based on both multi-agent simulation and 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.