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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

伊华伟 张付志 巢进波

伊华伟, 张付志, 巢进波. 基于模糊核聚类和支持向量机的鲁棒协同推荐算法[J]. 电子与信息学报, 2017, 39(8): 1942-1949. doi: 10.11999/JEIT161154
引用本文: 伊华伟, 张付志, 巢进波. 基于模糊核聚类和支持向量机的鲁棒协同推荐算法[J]. 电子与信息学报, 2017, 39(8): 1942-1949. doi: 10.11999/JEIT161154
YI Huawei, ZHANG Fuzhi, Chao Jinbo. Robust Collaborative Recommendation Algorithm Based on Fuzzy Kernel Clustering and Support Vector Machine[J]. Journal of Electronics and 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 and Information Technology, 2017, 39(8): 1942-1949. 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)

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: 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。
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    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

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