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An effective collaborative filtering algorithm based on user preference clustering

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Abstract

Collaborative filtering is one of widely used recommendation approaches to make recommendation services for users. The core of this approach is to improve capability for finding accurate and reliable neighbors of active users. However, collected data is extremely sparse in the user-item rating matrix, meanwhile many existing similarity measure methods using in collaborative filtering are not much effective, which result in the poor performance. In this paper, a novel effective collaborative filtering algorithm based on user preference clustering is proposed to reduce the impact of the data sparsity. First, user groups are introduced to distinguish users with different preferences. Then, considering the preference of the active user, we obtain the nearest neighbor set from corresponding user group/user groups. Besides, a new similarity measure method is proposed to preferably calculate the similarity between users, which considers user preference in the local and global perspectives, respectively. Finally, experimental results on two benchmark data sets show that the proposed algorithm is effective to improve the performance of recommender systems.

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Acknowledgments

The authors would like to thank the anonymous reviewers and the editor for their constructive and valuable comments. This work is supported by grants from the National Natural Science Foundation of China (Nos. 61303131, and 61379021), the Department of Education of Fujian Province (No. JA14129), and the Program for New Century Excellent Talents in Fujian Province University.

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Correspondence to Yaojin Lin.

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Zhang, J., Lin, Y., Lin, M. et al. An effective collaborative filtering algorithm based on user preference clustering. Appl Intell 45, 230–240 (2016). https://doi.org/10.1007/s10489-015-0756-9

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