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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
Xingxin Qin(),Rongbo Wang,Xiaoxi Huang,Zhiqun Chen
Institute of Cognitive and Intelligent Computing, Hangzhou Dianzi University, Hangzhou 310018, China
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Abstract  

[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

Cite this article:

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

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.06.07     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I6/65

[1] 王鹏, 王晶晶, 俞能海. 基于核方法的User-Based协同过滤推荐算法[J]. 计算机研究与发展, 2013, 50(7): 1444-1451.
[1] (Wang Peng, Wang Jingjing, Yu Nenghai.A Kernel and User-Based Collaborative Filtering Recommendation Algorithm[J]. Journal of Computer Research and Development, 2013, 50(7): 1444-1451.)
[2] 居斌, 钱沄涛, 叶敏超.基于结构投影非负矩阵分解的协同过滤算法[J]. 浙江大学学报: 工学版, 2015, 49(7): 1319-1325.
[2] (Ju Bin, Qian Yuntao, Ye Minchao.Collaborative Filtering Algorithm Based on Structured Projective Nonnegative Matrix Factorization[J]. Journal of Zhejiang University: Engineering Science, 2015, 49(7): 1319-1325.)
[3] 孙光明, 王硕, 邹静昭. 多因素复合度量的协同过滤推荐算法[J]. 计算机应用研究, 2015, 32(10): 2896-2900.
[3] (Sun Guangming, Wang Shuo, Zou Jingzhao.Collaborative Filtering Recommendation Algorithm Measured by Compound Multiple Fators[J]. Application Research of Computers, 2015, 32(10): 2896-2900.)
[4] Lemire D, Maclachlan A.Slope One Predictors for Online Rating-Based Collaborative Filtering[C]//Proceedings of the 2007 SIAM International Conference on Data Mining, Newport Beach, California, USA. 2007.
[5] 盈艳, 曹妍, 牟向伟. 基于项目评分预测的混合式协同过滤推荐[J]. 现代图书情报技术, 2015(6): 27-32.
[5] (Ying Yan, Cao Yan, Mou Xiangwei.A Hybrid Collaborative Filtering Recommender Based on Item Rating Prediction[J]. New Technology of Library and Information Service, 2015(6): 27-32.)
[6] Wang J, Lin K, Li J.A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Slope One Scheme[C]// Proceeding of the 8th International Conference on Computer Science Education. 2013.
[7] 张玉连, 郇思思, 梁顺攀. 融合用户相似度与项目相似度的加权Slope One算法[J]. 小型微型计算机系统, 2016, 37(6): 1174-1178.
[7] (Zhang Yulian, Huan Sisi, Liang Shunpan.Integrating User Similarity and Item Similarity into Weighted Slope One Algorithm[J]. Journal of Chinese Computer Systems, 2016, 37(6): 1174-1178.)
[8] 刘林静, 楼文高, 冯国珍. 基于用户相似性的加权Slope One算法[J].计算机应用研究, 2016, 33(9): 2708-2711.
[8] (Liu Linjing, Lou Wengao, Feng Guozhen.New Weighted Slope One Algorithm Based on User Similarity[J]. Application Research of Computers, 2016, 33(9): 2708-2711.)
[9] Finkenzeller K.RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification[M]. Hoboken: John Wiley & Sons, 2003.
[10] 邹永贵, 望靖, 刘兆宏, 等. 基于项目之间相似性的兴趣点推荐方法[J]. 计算机应用研究, 2012, 29(1): 116-118.
[10] (Zou Yonggui, Wang Jing, Liu Zhaohong, et al.Point of Interest Recommendation Method Based on Similarity Between Items[J]. Application Research of Computers, 2012, 29(1): 116-118.)
[11] 胡勋, 孟祥武, 张玉洁, 等. 一种融合项目特征和移动用户信任关系的推荐算法[J]. 软件学报, 2014, 25(8): 1817-1830.
[11] (Hu Xun, Meng Xiangwu, Zhang Yujie, et al.Recommendation Algorithm Combing Item Features and Trust Relationship of Mobile Users[J]. Journal of Software, 2014, 25(8): 1817-1830.)
[12] Herloker J L, Konstan J A, Terveen L G, et al.Evaluating Collaborative Filtering Recommender Systems[J]. ACM Transactions on lnformation System (TOIS), 2004, 22(1): 5-53.
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