Algorithm Research & Explore
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1643-1645,1651

Collaborative filtering algorithm combining filling and improving similarity

Xing Changzheng
Jin Yuan
School of Electronic Information Engineering, Liaoning Technical University, Huludao Liaoning 125105, China

Abstract

Aiming at the sparse user rating data, domestic and foreign scholars have made many improvements on collaborative filtering algorithm, which were summarized as filling user rating data, improving similarity, fusing content to recommend and so on. These single methods can't solve the problem of data sparseness. In order to solve this problem, this paper proposed a collaborative filtering algorithm which combines the filling data and improving similarity. Firstly, it used the improved filling method which increased the item's attribute information to fill the user rating data, and then recommended using new similarity method, produced the recommended results, iterated m times. Finally it recommended items according to the average score of scores got in m iterations. The experiment shows that the proposed algorithm has a better recommendation effect than single methods in the case of sparse user rating data.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.12.0813
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 6
Section: Algorithm Research & Explore
Pages: 1643-1645,1651
Serial Number: 1001-3695(2019)06-009-1643-03

Publish History

[2019-06-05] Printed Article

Cite This Article

邢长征, 金媛. 填补法和改进相似度相结合的协同过滤算法 [J]. 计算机应用研究, 2019, 36 (6): 1643-1645,1651. (Xing Changzheng, Jin Yuan. Collaborative filtering algorithm combining filling and improving similarity [J]. Application Research of Computers, 2019, 36 (6): 1643-1645,1651. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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