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

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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.

Key words： Robust recommendation algorithm Shilling attacks Matrix factorization Fuzzy Kernel Clustering (FKC) Support Vector Machine (SVM)

Received: 27 October 2016 Published: 11 May 2017

PACS:	TP3391
	TP311

Fund:

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)

Corresponding Authors: ZHANG Fuzhi E-mail: xjzfz@ysu.edu.cn

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	YI Huawei
	ZHANG Fuzhi
	Chao Jinbo

Cite this article:

YI Huawei,ZHANG Fuzhi,Chao Jinbo. Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine[J]. JEIT, 2017, 39(8): 1942-1949.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT161154 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I8/1942

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