WEIN 2010 - The Fifth International Workshop on Emergent Intelligence on Networked Agents
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AAMAS 2010 Satellite Workshop

The Fifth International Workshop on Emergent Intelligence on Networked Agents (WEIN 2010)

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

Akihiro Yamashita, Hidenori Kawamura and Keiji Suzuki

In many E-commerce sites, recommender systems, which provide personalized recommendation from among a large number of items, are recently introduced. Collaborative fltering (CF) is one of the most successful algorithms which provide recommendations using ratings of users on items. There are two approaches such as user-based CF and item-based CF. Additionally a unifying method for user-based and item-based CF was proposed to improve the recommendation accuracy. In the algorithm, a weight for unifying is a constant which obtained empirically. However, because the optimal weight for unifying is actually different by the situation, the algorithm should estimate an appropriate weight dynamically, and should use it. In this research, we propose an approach for estimation of the appropriate weight based on collected ratings. Moreover, we discussed the effectiveness based on both multi-agent simulation and MovieLens dataset.


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