Influential Neighbor Selection in Collaborative Filtering

doi:10.13190/j.jbupt.2016.01.005

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (1): 29-34.doi: 10.13190/j.jbupt.2016.01.005

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Influential Neighbor Selection in Collaborative Filtering

YANG Heng-yu¹, LI Hui-zong², LIN Yao-jin³, ZHANG Jia³

1. School of Computer and Information, Hefei University of Technology, Hefei 232009, China;
2. School of Economics and Management, Anhui University of Science and Technology, Anhui Huainan 232001, China;
3. School of Computer Science, Minnan Normal University, Fujian Zhangzhou 363000, China

Received:2015-04-10 Online:2016-02-28 Published:2016-01-29

Abstract

Abstract:

The recommendation performance of collaborative filtering is restricted by data sparsity. To solve this problem, the factor of user influence was thereafter defined according to the number of ratings to measure the relationship while calculating the similarity between users. Then, the influential user group was introduced according to the rating quality. On this basis, the chosen influential neighbor can work on the process of recommendations via combining the number of user ratings with the rating quality. Experiments show that the proposed method can significantly improve the recommendation performance.

Key words: collaborative filtering, influential neighbor, number of ratings, rating quality, data sparsity

CLC Number:

TP311

YANG Heng-yu, LI Hui-zong, LIN Yao-jin, ZHANG Jia. Influential Neighbor Selection in Collaborative Filtering[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(1): 29-34.

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Influential Neighbor Selection in Collaborative Filtering

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