Journal of East China Normal University(Natural Science) >

2018 , Vol. 2018 >Issue 4: 120 - 128,146

DOI: https://doi.org/10.3969/j.issn.1000-5641.2018.04.012

Collaborative filtering recommendation algorithm based on the self-similarity matrix

  • ZHANG Wei ,
  • ZHENG Jun ,
  • PANG Jiao-na ,
  • BAI Yue
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  • School of Computer Science and Software Engineering, East China Normal University, Shanghai 200062, China

Received date: 2017-07-12

  Online published: 2018-07-19

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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

Cite this article

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 Science), 2018 , 2018(4) : 120 -128,146 . DOI: 10.3969/j.issn.1000-5641.2018.04.012

References

[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.
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