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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (6): 65-71    DOI: 10.11925/infotech.2096-3467.2017.06.07
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Slope One Collaborative Filtering Algorithm Based on Multi-Weights
Qin Xingxin(), Wang Rongbo, Huang Xiaoxi, Chen Zhiqun
Institute of Cognitive and Intelligent Computing, Hangzhou Dianzi University, Hangzhou 310018, China
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[Objective] This paper aims to increase the recommendation accuracy with the help of modified Slope One algorithm. [Methods] We proposed a Slope One Collaboration Filtering Algorithm based on multi-weights, which improved the items’ similarity measure, attributes similarity measure and users’ rating probability function. Then, we combined the items’ similarity measure with the number of users and Pearson correlation coefficient, the items’ attributes similarity measure with modified Laplacian smoothing and Jaccard coefficient. We also identified users’ ratings with a new probability function. [Results] The proposed method reduced the MAE by 5.4%, which increased the recommendation accuracy. [Limitations] The new method did not examine the users’ comments, which might pose some negative effects to the recommendation accuracy. [Conclusions] The proposed algorithm could effectively improve the service of recommendation systems.

Key wordsCollaborative Filtering      Slope One      Multi-Weights      Item Similarity      Item Attributes     
Received: 26 April 2017      Published: 25 August 2017
ZTFLH:  TP391 G35  

Cite this article:

Qin Xingxin,Wang Rongbo,Huang Xiaoxi,Chen Zhiqun. Slope One Collaborative Filtering Algorithm Based on Multi-Weights. Data Analysis and Knowledge Discovery, 2017, 1(6): 65-71.

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