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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Adaptive Fusion Method for User-based and Item-based Collaborative Filtering
| Title: | Adaptive Fusion Method for User-based and Item-based Collaborative Filtering |
| Authors: | Yamashita, Akihiro Browse this author | | Kawamura, Hidenori Browse this author | | Suzuki, Keiji Browse this author |
| Keywords: | Recommender system | | collaborative filtering | | agent-based simulation |
| Issue Date: | Apr-2011 |
| Publisher: | World Scientific Publishing |
| Journal Title: | Advances in Complex Systems |
| Volume: | 14 |
| Issue: | 2 |
| Start Page: | 133 |
| End Page: | 149 |
| Publisher DOI: | 10.1142/S0219525911003001 |
| Abstract: | In many E-commerce sites, recommender systems, which provide personalized recommendations from among a large number of items, are recently introduced. Collaborative filtering 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 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 by the situation, the algorithm should estimate an appropriate weight dynamically, and should use it. In this research, first, we investigated the relationship between recommendation accuracy and the weight parameter. The results show 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. |
| Rights: | Electronic version of an article published as Advances in Complex Systems, 14(2), 2011, 133-149, 10.1142/S0219525911003001. © copyright World Scientific Publishing Company. http://www.worldscinet.com/acs/acs.shtml |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/45461 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 山下 晃弘
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