基于自相似矩阵的协同过滤推荐算法

doi:10.3969/j.issn.1000-5641.2018.04.012

华东师范大学学报(自然科学版) ›› 2018, Vol. 2018 ›› Issue (4): 120-128,146.doi: 10.3969/j.issn.1000-5641.2018.04.012

基于自相似矩阵的协同过滤推荐算法

张巍, 郑骏, 逄娇娜, 白玥

华东师范大学计算机科学与软件工程学院, 上海 200062

收稿日期:2017-07-12 出版日期:2018-07-25 发布日期:2018-07-19
通讯作者: 郑骏,男,教授,博士生导师,主要研究方向为,Web,开发及应用.E-mail:jzheng@cc.ecnu.edu.cn E-mail:jzheng@cc.ecnu.edu.cn
作者简介:张巍,男,硕士研究生,主要研究方向为Web开发及应用.E-mail:fj1weiwei@163.com

Collaborative filtering recommendation algorithm based on the self-similarity matrix

ZHANG Wei, ZHENG Jun, PANG Jiao-na, BAI Yue

School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

Received:2017-07-12 Online:2018-07-25 Published:2018-07-19

摘要/Abstract

摘要： 针对推荐系统中存在的噪声问题，提出了一种基于自相似矩阵的协同过滤推荐算法.文中的自相似矩阵选取为原始矩阵，滑动窗口选取为评分值的行向量和列向量.通过建立评分值与自相似矩阵之间的线性关系，对原始评分矩阵进行预处理，得到新的评分矩阵.新评分矩阵既保留了原始矩阵的评分信息，同时也削弱了噪声数据对推荐系统的影响.实验表明，通过对原始矩阵的预处理，有效缓解了噪声数据在评分矩阵中所起的作用，提高了推荐系统的性能.

关键词: 推荐系统, 协同过滤, 噪声数据, 自相似矩阵, 预处理

Abstract: A collaborative filtering recommendation algorithm based on self-similar matrices is put forward for the noise problem in the proposed system. In this paper, self-similar matrices are selected as primitive matrices, and the sliding window is chosen as the row vector and column vector of the score. The new score matrix is obtained to preprocess the original scoring matrix to establish the linear relationship between the scoring value and the self-similar matrices. The new scoring matrix preserves the original matrix of scoring information, while weakening the impact of noise data on the recommended system. Experiments show that the pre-processing of the original matrix effectively alleviates the impact of noise in the scoring matrix and improves the performance of the proposed system.

Key words: recommendation system, collaborative filtering, noise data, self-similarity matrix, pre-processing

中图分类号:

TP391

张巍, 郑骏, 逄娇娜, 白玥. 基于自相似矩阵的协同过滤推荐算法[J]. 华东师范大学学报(自然科学版), 2018, 2018(4): 120-128,146.

ZHANG Wei, ZHENG Jun, PANG Jiao-na, BAI Yue. Collaborative filtering recommendation algorithm based on the self-similarity matrix[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(4): 120-128,146.

参考文献

[1] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8):30-37.
[2] CACHEDA F, FORMOSO V. Comparison of collaborative filtering algorithms:Limitations of current techniques and proposals for scalable, high-performance recommender systems[J]. Acm Transactions on the Web, 2011, 5(1):1-33.
[3] GUO G, ZHANG J, YORKE-SMITH N. TrustSVD:Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings[C]//Twenty-Ninth AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA:AAAI Press, 2015:123-129.
[4] SARWAR B, KARYPIS G, KONSTAN J, et al. Application of dimensionality reduction in recommender systemsA case study[C]//Proc of the Acm Webkdd Workshop. New York:ACM, 2000.
[5] MASSA P, AVESANI P. Trust-aware collaborative filtering for recommender systems[M]//On the Move to Meaningful Internet Systems 2004, Coopis, DOA, and Odbase. Berlin:Springer, 2004:492-508.
[6] AMATRIAIN X, PUJOL J M, OLIVER N. I like it. I like it not:Evaluating user ratings noise in recommender systems[C]//International Conference on User Modeling, Adaptation, and Personalization:Formerly Um and Ah. Berlin:Springer-Verlag, 2009, 5535:247-258.
[7] XUE G R, LIN C, YANG Q, et al. Scalable collaborative filtering using cluster-based smoothing[C]//International Acm Sigir Conference on Research & Development in Information Retrieval. New York:ACM, 2005:114-121.
[8] ZHANG Z K, ZHOU T, ZHANG Y C. Tag-aware recommender systems:A state-of-the-art survey[J]. Journal of Computer Science and Technology, 2011, 26(5):767-777.
[9] UNGER M, BAR A, SHAPIRA B, et al. Towards latent context-aware recommendation systems[J]. Knowledge-Based Systems, 2016, 104:165-178.
[10] CHIRITA P A, NEJDL W, ZAMFIR C. Preventing shilling attacks in online recommender systems[C]//ACM International Workshop on Web Information and Data Management. New York:ACM, 2005:67-74.
[11] BILGE A, OZDEMIR Z, POLAT H. A novel shilling attack detection method[J]. Procedia Computer Science, 2014, 31:165-174.
[12] 刘江冬, 梁刚, 冯程,等. 基于信息熵和时效性的协同过滤推荐[J]. 计算机应用, 2016, 36(9):2531-2534.
[13] MA H, KING I, LYU M R. Effective missing data prediction for collaborative filtering[C]//International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2007:39-46.
[14] 朱郁筱, 吕琳媛. 推荐系统评价指标综述[J]. 电子科技大学学报, 2012, 41(2):163-175.
[15] KOREN Y. The bellkor solution to the netflix grand prize[J]. Netflix Prize Documentation, 2009(8):1-10.

基于自相似矩阵的协同过滤推荐算法

Collaborative filtering recommendation algorithm based on the self-similarity matrix

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