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

YI Huawei; ZHANG Fuzhi; Chao Jinbo

doi:10.11999/JEIT161154

Volume 39 Issue 8

Aug. 2017

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YI Huawei, ZHANG Fuzhi, Chao Jinbo. Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1942-1949. doi: 10.11999/JEIT161154

Citation:

YI Huawei, ZHANG Fuzhi, Chao Jinbo. Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1942-1949. doi: 10.11999/JEIT161154

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

doi: 10.11999/JEIT161154

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)

Received Date: 2016-10-27
Rev Recd Date: 2017-04-19
Publish Date: 2017-08-19

Abstract

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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References(32)

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