用于鲁棒协同推荐的元信息增强变分贝叶斯矩阵分解模型

李聪; 骆志刚

doi:10.3724/SP.J.1004.2011.01067

用于鲁棒协同推荐的元信息增强变分贝叶斯矩阵分解模型

doi: 10.3724/SP.J.1004.2011.01067

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

通讯作者:
李聪国防科学技术大学计算机学院博士研究生. 主要研究方向为机器学习,人工智能与信息检索.E-mail: licongwhy@gmail.com

计量
- 文章访问数: 2128
- HTML全文浏览量: 77
- PDF下载量: 1088
- 被引次数: 0
出版历程
- 收稿日期: 2010-11-29
- 修回日期: 2011-03-31
- 刊出日期: 2011-09-20

A Metadata-enhanced Variational Bayesian Matrix Factorization Model for Robust Collaborative Recommendation

LI Cong^,,
LUO Zhi-Gang

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

摘要

摘要: 托攻击是协同过滤推荐系统面临的重大安全威胁. 研究可抵御托攻击的鲁棒协同推荐技术已成为目前的重要课题. 本文在引入用户嫌疑性评估策略的基础上, 通过将用户嫌疑性及项类属等元信息与贝叶斯概率矩阵分解模型相融合, 提出了用于鲁棒协同推荐的元信息增强变分贝叶斯矩阵分解模型(Metadata-enhanced variational Bayesian matrix factorization, MVBMF), 并设计了相应的模型增量学习策略. 实验表明, 与现有推荐模型相比, 这种模型具备更强的攻击耐受力, 能够有效提高推荐系统的鲁棒性.
- 协同过滤 /
- 托攻击 /
- 矩阵分解 /
- 变分推断 /
- 鲁棒线性回归
Abstract: Shilling attacks pose a significant threat to the security of collaborative filtering recommender systems. It has come to be an important task to develop the attack-resistant techniques for robust collaborative recommendation. Through evaluating the user suspiciousness, and further integrating Bayesian probabilistic matrix factorization model with the metadata including user suspiciousness as well as item types, this paper proposes the metadata-enhanced variational Bayesian matrix factorization (MVBMF) model for robust collaborative recommendation, and designs the corresponding incremental learning strategy. Experimental results show that comparing with the existed recommendation models, this model has stronger resistibility and can effectively improve the robustness of recommender systems.
- Collaborative filtering /
- shilling attacks /
- matrix factorization /
- variational inference /
- robust linear regression

HTML全文

参考文献(1)

[1]

Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extension. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749[2] 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[3] O'Mahony M P, Hurley N J, Silvestre G C M. Detecting noise in recommender system databases. In: Proceedings of the 11th International Conference on Intelligent User Interfaces. Sydney, Australia: ACM, 2006. 109-115[4] Lam S, Riedl J. Shilling recommender systems for fun and profit. In: Proceedings of the 13th Conference on World Wide Web. New York, USA: ACM, 2004. 393-402[5] Su X, Khoshgoftaar T M. A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 2009: 1-20[6] Mobasher B, Burke R, 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[7] Mobasher B, Burke R, Bhaumik R, Sandvig J J. Attacks and remedies in collaborative recommendation. IEEE Intelligent Systems, 2007, 22(3): 56-63[8] O'Mahony M P, Hurley N J, Silvestre G C M. Efficient and secure collaborative filtering through intelligent neighbor selection. In: Proceedings of the 16th European Conference on Artificial Intelligence. Valencia, Spain: IOS Press, 2004. 383-387[9] Sandvig J J, Mobasher B, Burke R. Impact of relevance measures on the robustness and accuracy of collaborative filtering. In: Proceedings of the 8th International Conference on E-commerce and Web Technologies. Berlin, Germany: Springer, 2007. 99-108[10] Sandvig J J, Mobasher B, Burke R. Robustness of collaborative recommendation based on association rule mining. In: Proceedings of the ACM Conference on Recommender Systems. New York, USA: ACM, 2007. 105-112[11] Mehta B, Hofmann T, Nejdl W. Robust collaborative filtering. In: Proceedings of the ACM Conference on Recommender Systems. New York, USA: ACM, 2007. 49-56[12] Huber P J. Robust estimation of a location parameter. The Annals of Mathematical Statistics, 1964, 35(1): 73-101[13] Mehta B, Nejdl W. Attack resistant collaborative filtering. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2008. 75-82[14] Hofmann T. Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1999. 50-57[15] Hurley N, Cheng Z, Zhang M. Statistical attack detection. In: Proceedings of the 3rd ACM Conference on Recommender Systems. New York, USA: ACM, 2009. 149-156[16] Zhang S, Wang W, Ford J, Makedon F, Pearlman J. Using singular value decomposition approximation for collaborative filtering. In: Proceedings of the 7th IEEE International Conference on E-commerce Technology. Washington D.C., USA: IEEE, 2005. 257-264[17] Salakhutdinov R, Mnih A. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th International Conference on Machine Learning. New York, USA: ACM, 2008. 880-887[18] Williamson S, Ghahramani Z. Probabilistic models for data combination in recommender systems. In: Proceedings of the NIPS Workshop: Learning from Multiple Sources. Vancouver, Canada: The MIT Press, 2008. 1-4[19] Tipping M E, Lawrence N D. Variational inference for student-t models: robust Bayesian interpolation and generalised component analysis. Neurocomputing, 2005, 69(1-3): 123-141[20] Attias H. Inferring parameters and structure of latent variable models by variational Bayes. In: Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 1999. 21-30[21] Jaakkola T S, Jordan M I. Bayesian parameter estimation via variational methods. Statistics and Computing, 2000, 10(1): 25-37[22] Marlin B. Collaborative Filtering: a Machine Learning Perspective [Master dissertation], University of Toronto, Canada, 2004[23] Huber P J, Ronchetti E M. Robust Statistics (Second Edition). New Jersey: John Wiley and Sons, 2009. 149-199[24] Barrodale I, Roberts F D K. An improved algorithm for discrete L_1 linear approximation. SIAM Journal on Numerical Analysis, 1973, 10(5): 839-848[25] Street J O, Carroll R J, Ruppert D. A note on computing robust regression estimates via iteratively reweighted least squares. The American Statistician, 1988, 42(2): 152-154[26] MacKay D J C. Bayesian interpolation. Neural Computation, 1992, 4(3): 415-447

施引文献

资源附件(0)

访问统计