Title: A research on the improved slope one algorithm for collaborative filtering

Authors: Yanni Liu; Dongsheng Liu; Honghua Xie; Liming Wang

Addresses: School of Computer and Information Engineering, Zhejiang Gongshang University, China; School of Foreign Languages, Zhejiang Gongshang University, No. 18, Xuezheng Str., Xiasha University Town, Hangzhou, China ' School of Computer and Information Engineering, Zhejiang Gongshang University, China; Contemporary Business and Trade Research Center, Zhejiang Gongshang University, No. 18, Xuezheng Str., Xiasha University Town, Hangzhou, China ' Hangzhou Yiyatong Technology Co. Ltd., No. 59, Shixiang Road, Hangzhou, China ' Hangzhou College of Commerce, Zhejiang Gongshang University, No. 18, Xuezheng Str., Xiasha University Town, Hangzhou, China

Abstract: In this paper, as an algorithm used for collaborative filtering, there are some shortcomings about slope one algorithm in commercial recommendation system, such as the rating predictions without considering the behaviours and attributes of the users and item, and data sparsity. We proposed the improved slope one algorithm based on the singular value decomposition technique and item similarity to improve the algorithm and process. Then the implementation scheme and flow chart of the improved algorithm is given. Finally, the new algorithm is evaluated by four different datasets. The result shows that in sparse datasets the improved slope one algorithm is more precise than slope one algorithm. In addition, in the four datasets with different sparsity degree, the improved slope one algorithm is stable, and the change of MAE value is relatively stable.

Keywords: collaborative filtering; slope one algorithm; singular value decomposition; SVD; item similarity; rating matrix; sparse datasets.

DOI: 10.1504/IJCSM.2016.077865

International Journal of Computing Science and Mathematics, 2016 Vol.7 No.3, pp.245 - 253

Received: 22 Jan 2016
Accepted: 25 Apr 2016
Published online: 17 Jul 2016 *

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