基于模糊核聚類和支持向量機(jī)的魯棒協(xié)同推薦算法
doi: 10.11999/JEIT161154
基金項(xiàng)目:
國家自然科學(xué)基金(61379116)，河北省自然科學(xué)基金(F2015203046)，遼寧省教育廳科學(xué)研究項(xiàng)目(L2015240)
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)
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摘要: 該文針對(duì)現(xiàn)有推薦算法在面對(duì)托攻擊時(shí)魯棒性不高的問題，提出一種基于模糊核聚類和支持向量機(jī)的魯棒推薦算法。首先，根據(jù)攻擊概貌間高度相關(guān)的特性，利用模糊核聚類方法在高維特征空間對(duì)用戶概貌進(jìn)行聚類，實(shí)現(xiàn)攻擊概貌的第1階段檢測(cè)。然后，利用支持向量機(jī)分類器對(duì)含有攻擊概貌的聚類進(jìn)行分類，實(shí)現(xiàn)攻擊概貌的第2階段檢測(cè)。最后，基于攻擊概貌檢測(cè)結(jié)果，通過構(gòu)造指示函數(shù)排除攻擊概貌在推薦過程中產(chǎn)生的影響，并引入矩陣分解技術(shù)設(shè)計(jì)相應(yīng)的魯棒協(xié)同推薦算法。實(shí)驗(yàn)結(jié)果表明，與現(xiàn)有的基于矩陣分解模型的推薦算法相比，所提算法不但具有很好的魯棒性，而且準(zhǔn)確性也有提高。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.
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