基于数据非随机缺失机制的推荐系统托攻击探测

李聪; 骆志刚

doi:10.3724/SP.J.1004.2013.01681

基于数据非随机缺失机制的推荐系统托攻击探测

doi: 10.3724/SP.J.1004.2013.01681

李聪,
骆志刚

1.
国防科学技术大学计算机学院长沙 410073

详细信息

作者简介:
骆志刚国防科学技术大学计算机学院教授.主要研究方向为高性能计算,数据挖掘与生物信息学.E-mail:zgluo@nudt.edu.cn

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出版历程
- 收稿日期: 2011-02-28
- 修回日期: 2012-06-29
- 刊出日期: 2013-10-20

Detecting Shilling Attacks in Recommender Systems Based on Non-random-missing Mechanism

1.
School of Computer, National University of Defense Technology, Changsha 410073

摘要

摘要: 协同过滤推荐系统极易受到托攻击的侵害. 开发托攻击探测技术已成为保障推荐系统可靠性与鲁棒性的关键. 本文以数据非随机缺失机制为依托,对导致评分缺失的潜在因素进行解析, 并在概率产生模型框架内将这些潜在因素与Dirichlet过程相融合, 提出了用于托攻击探测的缺失评分潜在因素分析(Latent factor analysis for missing ratings, LFAMR)模型. 实验表明,与现有探测技术相比, LFAMR具备更强的普适性和无监督性, 即使缺乏系统相关先验知识,仍可有效探测各种常见托攻击.
- 协同过滤 /
- 托攻击 /
- 缺失数据 /
- Dirichlet过程 /
- 变分推断
Abstract: Collaborative filtering recommender systems are highly vulnerable to shilling attacks. Developing detection techniques against shilling attacks has become the key to guaranteeing both the reliability and robustness of recommender systems. Through revealing the latent factors invoking missing ratings under the non-random-missing mechanism, and further combining these latent factors with Dirichlet process in the framework of probabilistic generative model, this paper proposes a latent factor analysis for missing ratings (LFAMR) model for attack detection. Experimental results show that comparing with the existing detection techniques, LFAMR is more universal and unsupervised, and that it can effectively detect shilling attacks of typical types even in lack of system-related prior knowledge.
- Collaborative filtering /
- shilling attacks /
- missing data /
- Dirichlet process /
- variational inference

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参考文献(25)

[1]	Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749
[2]	Su X Y, Khoshgoftaar T M. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 2009: 1-20
[3]	Mobasher B, Burke R, Bhaumik R, Sandvig J J. Attacks and remedies in collaborative recommendation. IEEE Intelligent Systems, 2007, 22(3): 56-63
[4]	Lam S K, Riedl J. Shilling recommender systems for fun and profit. In: Proceedings of the 13th International Conference on World Wide Web. New York, USA: ACM, 2004. 393-402
[5]	O'Mahony M P, Hurley N J, Kushmerick N, Silvestre G C M. Collaborative recommendation: a robustness analysis. ACM Transactions on Internet Technology, 2004, 4(4): 344-377
[6]	Huang Z, Chen H, Zeng D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 2004, 22(1): 116-142
[7]	Mobasher B, Burke R D, Bhaumik R, Williams C. Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology, 2007, 7(4): 1-40
[8]	Burke R, Mobasher B, Williams C, Bhaumik R. Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Philadelphia, Pennsylvania, USA: ACM, 2006. 542-547
[9]	Zhang S, Ouyang Y, Ford J, Makedon F. Analysis of a low-dimensional linear model under recommendation attacks. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Seattle, Washington, USA: ACM, 2006. 517-524
[10]	Mehta B, Nejdl W. Unsupervised strategies for shilling detection and robust collaborative filtering. User Modeling and User-Adapted Interaction, 2009, 19(1-2): 65-97
[11]	Mehta B, Hofmann T, Fankhauser P. Lies and propaganda: detecting spam users in collaborative filtering. In: Proceedings of the 12th International Conference on Intelligent User Interfaces. Honolulu, Hawaii: ACM, 2007. 14-21
[12]	Bryan K, O'Mahony M P, Cunningham P. Unsupervised retrieval of attack profiles in collaborative recommender systems. In: Proceedings of the 2008 ACM Conference on Recommender Systems. Lausanne, Switzerland: ACM, 2008. 155-162
[13]	Little R J A, Rubin D B. Statistical Analysis with Missing Data. New York: John Wiley, 1987. 13-17
[14]	Ferguson T S. A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1973, 1(2): 209-230
[15]	MacKay D J C. Bayesian interpolation. Neural Computation, 1992, 4(3): 415-447
[16]	Frigyik B A, Kapila A, Gupta M R. Introduction to the Dirichlet Distribution and Related Processes, Technical Report, Department of Electrical Engineering, University of Washington, 2010
[17]	Sethuraman J. A constructive definition of Dirichlet priors. Statistica Sinica, 1994, 4: 639-650
[18]	Antoniak C E. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics, 1974, 2(6): 1152-1174
[19]	Attias H. Inferring parameters and structure of latent variable models by variational bayes. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA, USA: Morgan Kaufmann, 1999. 21-30
[20]	Jaakkola T S, Jordan M I. Bayesian parameter estimation via variational methods. Statistics and Computing, 2000, 10(1): 25-37
[21]	Ishwaran J, James L F. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 2001, 96(453): 161-173
[22]	Blei D M, Jordan M I. Variational inference for Dirichlet process mixtures. Bayesian Analysis, 2006, 1(1): 121-144
[23]	Lewis D D, Gale W A. A sequential algorithm for training text classifiers. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland: Springer, 1994. 3-12
[24]	Fisher R A. The use of multiple measurements in taxonomic problems. Annals of Eugenics, 1936, 7(2): 179-188
[25]	Mehta B, Hofmann T. A survey of attack-resistant collaborative filtering algorithms. IEEE Data Engineering Bulletin, 2008, 31(2): 14-22