CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

Wei Zeng (), Ming-Sheng Shang (), Qian-Ming Zhang (), Linyuan Lü () and Tao Zhou ()
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Wei Zeng: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
Ming-Sheng Shang: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
Qian-Ming Zhang: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
Linyuan Lü: Department of Physics, University of Fribourg, Chemin du Musée 3, Fribourg CH-1700, Switzerland
Tao Zhou: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China;

International Journal of Modern Physics C (IJMPC), 2010, vol. 21, issue 10, 1217-1227

Abstract: Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

Keywords: Recommender systems; collaborative filtering; diversity; accuracy; 89.20.Ff; 89.75.Hc; 89.65.-s (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0129183110015786

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