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Information filtering via collaborative user clustering modeling

Chu-Xu Zhang, Zi-Ke Zhang, Lu Yu, Chuang Liu, Hao Liu and Xiao-Yong Yan

Physica A: Statistical Mechanics and its Applications, 2014, vol. 396, issue C, 195-203

Abstract: The past few years have witnessed the great success of recommender systems, which can significantly help users to find out personalized items for them from the information era. One of the widest applied recommendation methods is the Matrix Factorization (MF). However, most of the researches on this topic have focused on mining the direct relationships between users and items. In this paper, we optimize the standard MF by integrating the user clustering regularization term. Our model considers not only the user-item rating information but also the user information. In addition, we compared the proposed model with three typical other methods: User-Mean (UM), Item-Mean (IM) and standard MF. Experimental results on two real-world datasets, MovieLens 1M and MovieLens 100k, show that our method performs better than other three methods in the accuracy of recommendation.

Keywords: Recommender systems; Collaborative filtering; Matrix factorization; User clustering regularization (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:396:y:2014:i:c:p:195-203

DOI: 10.1016/j.physa.2013.11.024

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