Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine
YI Huawei①②③ ZHANG Fuzhi①② Chao Jinbo①②
①(School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China) ②(Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province (Yanshan University), Qinhuangdao 066004, China) ③(School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China)
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.
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