融合随机森林和目标项目识别的鲁棒推荐算法

doi:10.6040/j.issn.1671-9352.2.2020.018

摘要/Abstract

摘要： 针对协同推荐算法面对托攻击时鲁棒性差的问题,提出一种融合随机森林和目标项目识别的鲁棒推荐算法。首先,基于卡方统计理论提取能够区分正常用户与攻击用户的有效特征。然后,训练随机森林分类器对攻击概貌进行第一阶段检测。接下来,通过识别目标项目对含有攻击概貌的类别做进一步检测,实现攻击概貌的第二阶段检测。最后,根据攻击概貌检测结果构建鲁棒推荐算法。实验结果表明,所提算法在保障推荐精度的前提下具有较强的鲁棒性。

关键词: 鲁棒推荐, 随机森林, 目标项目, 卡方统计, 攻击检测

Abstract: The collaborative recommendation algorithms have lower robustness in the presence of shilling attacks. To address this problem, a robust recommendation algorithm combining random forest and target item identification is proposed. Firstly, chi-square statistics is used to extract the effective features which can distinguish normal users and attacking users. Then, the random forest classifier is used to classify user profiles, which is the first stage of the attack profile detection. Next, the further detection to the cluster including attack profiles is performed by identifying the target item, which is the second stage of the attack profile detection. Finally, based on the detection results of attack profiles, the corresponding robust recommendation algorithm is constructed. Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

Key words: robust recommendation, random forest, target item, chi-square statistics, attack detection

中图分类号:

TP311

伊华伟,牛在森,李晓会,李波,景荣. 融合随机森林和目标项目识别的鲁棒推荐算法[J]. 《山东大学学报(理学版)》, 2022, 57(5): 46-56.

YI Hua-wei, NIU Zai-sen, LI Xiao-hui, LI Bo, JING Rong. Robust recommendation algorithm combining random forest and target item identification[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2022, 57(5): 46-56.

参考文献

[1] XIAO Teng, SHEN Hong. Neural variational matrix factorization for collaborative filtering in recommendation systems[J]. Applied Intelligence, 2019, 49(6):3558-3569.
[2] CHEN Li, CHEN Guanliang, WANG Feng. Recommender systems based on user reviews: the state of the art[J]. User Modeling and User-Adapted Interaction, 2015, 25(2):99-154.
[3] GUNES I, KALELI C, BILGE A, et al. Shilling attacks against recommender systems: a comprehensive survey[J]. Artificial Intelligence Review, 2014, 42(4):767-799.
[4] LEE J S, ZHU D. Shilling attack detection: a new approach for a trustworthy recommender system[J]. Informs Journal on Computing, 2012, 24(1):117-131.
[5] MOBASHER B, BURKE R, SANDVIG JJ. Model-based collaborative filtering as a defense against profile injection attacks[C] //Proceedings of the 21st National Conference on Artificial Intelligence, Boston, [s.n.] : 2006: 1388-1393.
[6] OMAHONY M, HURLEY N, KUSHMERICK N, et al. Collaborative recommendation: a robustness analysis[J]. ACM Transactions on Internet Technology, 2004, 4(4):344-377.
[7] CHAD A W, BAMSHAD M, ROBIN B. Defending recommender systems: detection of profile injection attacks[J]. Service Oriented Computing and Applications, 2007, 1(3):157-170.
[8] WILLIAMS C A, MOBASHER B, BURKE R, et al. Detecting profile injection attacks in collaborative filtering: a classification-based approach[C] //Proceedings of the 8th Knowledge Discovery on the Web International Conference on Advances in Web Mining and Web Usage Analysis. Berlin: Springer, 2007: 167-186.
[9] 伍之昂,庄毅,王有权,等. 基于特征选择的推荐系统托攻击检测算法[J]. 电子学报, 2012, 40(8):1687-1693. WU Zhiang, ZHUANG Yi, WANG Youquan, et al. Shilling attack detection based on feature selection for recommendation systems[J]. Acta Electronica Sinica, 2012, 40(8):1687-1693.
[10] 李文涛,高旻,李华,等. 一种基于流行度分类特征的托攻击检测算法[J]. 自动化学报, 2015, 41(9):1563-1576. LI Wentao, GAO Min, LI Hua, et al. An shilling attack detection algorithm based on popularity degree features[J]. Acta Automatica Sinica, 2015, 41(9):1563-1576.
[11] ZHOU Quanqiang.Supervised approach for detecting average over popular items attack in collaborative recommender systems[J]. IET Information Security, 2016, 10(3):134-141.
[12] HAO Yaojun, ZHANG Fuzhi, CHAO Jinbo. An ensemble detection method for shilling attacks based on features of automatic extraction[J]. China Communications, 2019, 16(8):130-146.
[13] ZHANG Fuzhi, ZHANG Zening, ZHANG Peng, et al. UD-HMM: an unsupervised method for shilling attack detection based on hidden markov model and hierarchical clustering[J]. Knowledge-Based Systems, 2018, 148:146-166.
[14] 李聪, 骆志刚. 基于数据非随机缺失机制的推荐系统托攻击探测[J]. 自动化学报, 2013, 39(10):1681-1690. LI Cong, LUO Zhigang. Detecting shilling attacks in recommender systems based on non-random-missing mechanism[J]. Acta Automatica Sinica, 2013, 39(10):1681-1690.
[15] 周全强, 张付志. 基于仿生模式识别的用户概貌攻击集成检测方法[J]. 计算机研究与发展, 2014, 51(4):789-801. ZHOU Quanqiang, ZHANG Fuzhi. Ensemble approach for detecting user profile attacks based on bionic pattern recognition[J]. Journal of Computer Research and Development, 2014, 51(4):789-801.
[16] ZHANG Fuzhi, LING Zhoujun, WANG Shilei. An unsupervised approach for detecting shilling attacks in collaborative recommender systems based on user rating behaviours[J]. IET Information Security, 2018, 13(3):174-187.
[17] KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8):30-37.
[18] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32.
[19] YANG Y, PEDERSEN J O. A comparative study on feature selection in text categorization[C] //Proceedings of the Fourteenth International Conference on Machine Learning, San Francisco, [s.n.] : 1997:412-420.
[20] DUNNING T. Accurate methods for the statistics of surprise and coincidence[J]. Computational Linguistics, 1993, 19(1):61-74.
[21] 牛在森,伊华伟,李晓会,等. 融合层次聚类和粒子群优化的鲁棒推荐算法[J]. 小型微型计算机系统, 2020, 41(1):68-71. NIU Zaisen, YI Huawei, LI Xiaohui, et al. Robust recommendation algorithm combining hierarchical clustering and particle swarm optimization[J]. Journal of Chinese Computer Systems, 2020, 41(1):68-71.
[22] MILLER BN, ALBERT I, LAM SK, et al. MovieLens unplugged: experiences with an occasionally connected recommender system[C] //Proceedings of the 8th International Conference on Intelligent User Interfaces, New York: ACM, 2003: 263-266.
[23] RICCI F, ROKACH L, SHAPIRA B, et al. Recommender systems handbook[M]. Berlin: Springer, 2011: 145-186.
[24] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874.
[25] CHENG Z, HURLEY N. Robust collaborative recommendation by least trimmed squares matrix factorization[C] //Proceedings of the IEEE 2010 22nd International Conference on Tools with Artificial Intelligence(ICTAI). Arras:[s.n.] , 2010: 105-112.
[26] XU Yuchen, LIU Zhen, ZHANG Fuzhi. Robust recommendation approach based on incremental clustering and matrix factorization [J]. Journal of Chinese Computer Systems, 2015, 36(4):689-695.
[27] 赵明,闫寒,曹高峰,等. 融合用户信任度和相似度的基于核心用户抽取的鲁棒性推荐算法[J]. 电子与信息学报, 2019, 41(1):180-186. ZHAO Ming, YAN Han, CAO Gaofeng, et al. Robust recommendation algorithm based on core user extraction with user trust and similarity[J]. Journal of Electronics & Information Technology, 2019, 41(1):180-186.

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