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基于数据非随机缺失机制的推荐系统托攻击探测

李聪 骆志刚

李聪, 骆志刚. 基于数据非随机缺失机制的推荐系统托攻击探测. 自动化学报, 2013, 39(10): 1681-1690. doi: 10.3724/SP.J.1004.2013.01681
引用本文: 李聪, 骆志刚. 基于数据非随机缺失机制的推荐系统托攻击探测. 自动化学报, 2013, 39(10): 1681-1690. doi: 10.3724/SP.J.1004.2013.01681
LI Cong, LU Zhi-Gang. Detecting Shilling Attacks in Recommender Systems Based on Non-random-missing Mechanism. ACTA AUTOMATICA SINICA, 2013, 39(10): 1681-1690. doi: 10.3724/SP.J.1004.2013.01681
Citation: LI Cong, LU Zhi-Gang. Detecting Shilling Attacks in Recommender Systems Based on Non-random-missing Mechanism. ACTA AUTOMATICA SINICA, 2013, 39(10): 1681-1690. doi: 10.3724/SP.J.1004.2013.01681

基于数据非随机缺失机制的推荐系统托攻击探测

doi: 10.3724/SP.J.1004.2013.01681
详细信息
    作者简介:

    骆志刚 国防科学技术大学计算机学院教授.主要研究方向为高性能计算,数据挖掘与生物信息学.E-mail:zgluo@nudt.edu.cn

Detecting Shilling Attacks in Recommender Systems Based on Non-random-missing Mechanism

  • 摘要: 协同过滤推荐系统极易受到托攻击的侵害. 开发托攻击探测技术已成为保障推荐系统可靠性与鲁棒性的关键. 本文以数据非随机缺失机制为依托,对导致评分缺失的潜在因素进行解析, 并在概率产生模型框架内将这些潜在因素与Dirichlet过程相融合, 提出了用于托攻击探测的缺失评分潜在因素分析(Latent factor analysis for missing ratings, LFAMR)模型. 实验表明,与现有探测技术相比, LFAMR具备更强的普适性和无监督性, 即使缺乏系统相关先验知识,仍可有效探测各种常见托攻击.
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出版历程
  • 收稿日期:  2011-02-28
  • 修回日期:  2012-06-29
  • 刊出日期:  2013-10-20

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