基于模糊核聚类和支持向量机的鲁棒协同推荐算法

伊华伟; 张付志; 巢进波

doi:10.11999/JEIT161154

基于模糊核聚类和支持向量机的鲁棒协同推荐算法

doi: 10.11999/JEIT161154

基金项目:

国家自然科学基金(61379116)，河北省自然科学基金(F2015203046)，辽宁省教育厅科学研究项目(L2015240)

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- 文章访问数: 1330
- HTML全文浏览量: 153
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- 被引次数: 0
出版历程
- 收稿日期: 2016-10-27
- 修回日期: 2017-04-19
- 刊出日期: 2017-08-19

Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine

Funds:

The National Natural Science Foundation of China (61379116), The Natural Science Foundation of Hebei Province (F2015203046), The Scientific Research Foundation of Liaoning Provincial Education Department (L2015240)

摘要

摘要: 该文针对现有推荐算法在面对托攻击时鲁棒性不高的问题，提出一种基于模糊核聚类和支持向量机的鲁棒推荐算法。首先，根据攻击概貌间高度相关的特性，利用模糊核聚类方法在高维特征空间对用户概貌进行聚类，实现攻击概貌的第1阶段检测。然后，利用支持向量机分类器对含有攻击概貌的聚类进行分类，实现攻击概貌的第2阶段检测。最后，基于攻击概貌检测结果，通过构造指示函数排除攻击概貌在推荐过程中产生的影响，并引入矩阵分解技术设计相应的鲁棒协同推荐算法。实验结果表明，与现有的基于矩阵分解模型的推荐算法相比，所提算法不但具有很好的鲁棒性，而且准确性也有提高。
- 鲁棒推荐算法 /
- 托攻击 /
- 矩阵分解 /
- 模糊核聚类 /
- 支持向量机
Abstract: The existing collaborative recommendation algorithms have low robustness against shilling attacks. To solve this problem, a robust collaborative recommendation algorithm is proposed based on Fuzzy Kernel Clustering (FKC) and Support Vector Machine (SVM). Firstly, according to the high correlation characteristic between attack profiles, the FKC method is used to cluster user profiles in high-dimensional feature space, which is the first stage of the attack profile detection. Then, the SVM classifier is used to classify the cluster including attack profiles, which is the second stage of the attack profile detection. Finally, an indicator function is constructed based on the attack detection results to reduce the influence of attack profiles on the recommendation, and it is combined with the matrix factorization technology to devise the corresponding robust collaborative recommendation algorithm. Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.
- Robust recommendation algorithm /
- Shilling attacks /
- Matrix factorization /
- Fuzzy Kernel Clustering (FKC) /
- Support Vector Machine (SVM)

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参考文献(32)

孟祥武, 刘树栋, 张玉洁, 等. 社会化推荐系统研究[J]. 软件学报, 2015, 26(6): 1356-1372.

MENG Xiangwu, LIU Shudong, ZHANG Yujie, et al. Research on social recommendation systems[J]. Journal of Software, 2015, 26(6): 1356-1372.

CHEN L, CHEN G L, WANG F. Recommender systems based on user reviews: The state of the art[J]. User Modeling and User-Adapted Interaction, 2015, 25(2): 99-154. doi: 10.1007/s11257-015-9155-5.

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. doi: 10.1007/s10462-012-9364-9.

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. doi: 10.1145/1031114.1031116.

MEHTA B and NEJDL W. Attack resistant collaborative filtering[C]. Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, 2008: 75-82.

LEE J and ZHU D. Shilling attack detection-a new approach for a trustworthy recommender system[J]. Informs Journal on Computing, 2012, 24(1): 117-131. doi: 10.1287/ijoc.1100. 0440.

BHAUMIK R, MOBASHER B, and BURKE R. A clustering approach to unsupervised attack detection in collaborative recommender systems[C]. Proceedings of the 7th International Conference on Data Mining, IEEE Computer Society, Washington: 2011: 181-187.

李聪, 骆志刚, 石金龙. 一种探测推荐系统托攻击的无监督算法[J]. 自动化学报, 2011, 37(2): 160-167.

LI Cong, LUO Zhigang, and SHI Jinlong. An unsupervised algorithm for detecting shilling attacks on recommender systems[J]. Acta Automatica Sinica, 2011, 37(2): 160-167.

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, 2007: 167-186.

WILLIAMS C, MOBASHER B, and BURKE R. Defending recommender systems: Detection of profile injection attacks [J]. Service Oriented Computing and Applications, 2007, 1(3): 157-170. doi: 10.1007/s11761-007-0013-0.

HE F, WANG X, and LIU B. Attack detection by rough set theory in recommendation system[C]. 2010 IEEE International Conference on Granular Computing, Washington, 2010: 692-695.

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. doi: 10.3969/ j.issn.0372-2112.2012.08.031.

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. doi: 10.3969/j.issn.0372-2112.2012.08.031.

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. 自动化学报, 2015, 41(9): 1563-1575.

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): 1563-1575. doi: 10.16383/j.aas.2015.c150040.

ZHANG F and ZHOU Q. Ensemble detection model for profile injection attacks in collaborative recommender systems based on BP neural network[J]. Iet Information Security, 2015, 9(1): 24-31. doi: 10.1049/iet-ifs.2013.0145.

SANDVIG J J, MOBASHER B, and BURKE R. A survey of collaborative recommendation and the robustness of model-based algorithms[J]. Bulletin of the Technical Committee on Data Engineering, 2008, 31(2): 3-13.

SANDVIG J J, MOBASHER B, and BURKE R. Robustness of collaborative recommendation based on association rule mining[C]. Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, 2007: 105112.

MEHTA B, HOFMANN T, and NEJDL W. Robust collaborative filtering[C]. ACM Conference on Recommender Systems, Recsys, Minneapolis, MN, USA, 2007: 49-56.

CHENG Z and HURLEY N. Robust collaborative recommendation by least trimmed squares matrix factorization[C]. Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence, Arras, France, 2010: 105-112.

YI Huawei and ZHANG Fuzhi. A robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator[J]. China Communications, 2014, 11(9): 119-130. doi: 10.1109/CC.2014.6969776.

李聪, 骆志刚. 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[J]. 自动化学报, 2011, 37(9): 1067-1076.

LI Cong and LUO Zhigang. A metadata-enhanced variational Bayesian matrix factorization model for robust collaborative recommendation[J]. Acta Automatica Sinica, 2011, 37(9): 1067-1076.

张燕平, 张顺, 钱付兰, 等. 基于用户声誉的鲁棒协同推荐算法[J]. 自动化学报, 2015, 41(5): 1004-1012. doi: 10.16383/j. aas.2015.c140073.

ZHANG Yanping, ZHANG Shun, QIAN Fulan, et al. Robust collaborative recommendation algorithm based on users reputation[J]. Acta Automatica Sinica, 2015, 41(5): 1004-1012. doi: 10.16383/j.aas.2015.c140073.

李改, 李磊. 鲁棒的单类协同排序算法[J]. 自动化学报, 2015, 41(2): 405-418. doi: 10.16383/j.aas.2015.c140231.

LI Gai and LI Lei. Robust ranking algorithms for one-class collaborative filtering[J]. Acta Automatica Sinica, 2015, 41(2): 405-418. doi: 10.16383/j.aas.2015.c140231.

YI H and ZHANG F. Robust recommendation algorithm based on the identification of suspicious users and matrix factorization[J]. Journal of Information and Computational Science, 2014, 11(13): 4769-4777. doi: 10.12733/ JICS20104307.

RICCI F, SHAPIRA B, and ROKACH L. Recommender Systems Handbook[M]. New York, Springer US, 2015: 961-995. doi: 10.1007/978-1-4899-7637-6_28.

DESHPANDE M and KARYPIS G. Item-based top-N recommendation algorithms[J]. ACM Transactions on Information Systems, 2004, 22(1): 143-177.

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文章访问数: 1330
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基于模糊核聚类和支持向量机的鲁棒协同推荐算法

doi: 10.11999/JEIT161154

计量

Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine

计量

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留言板

基于模糊核聚类和支持向量机的鲁棒协同推荐算法

doi: 10.11999/JEIT161154

计量

出版历程

Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine

计量

出版历程

目录