融合随机森林和目标项目识别的鲁棒推荐算法  

Robust recommendation algorithm combining random forest and target item identification

作　　者：伊华伟[1] 牛在森 李晓会[1] 李波[1] 景荣[2] YI Hua-wei;NIU Zai-sen;LI Xiao-hui;LI Bo;JING Rong(School of Electronics and Information Engineering,Liaoning University of Technology,Jinzhou 121001,Liaoning,China;School of Information Science and Engineering,Yanshan University,Qinhuangdao 066044,Hebei,China)

机构地区：[1]辽宁工业大学电子与信息工程学院,辽宁锦州121001 [2]燕山大学信息科学与工程学院,河北秦皇岛066044

出　　处：《山东大学学报（理学版）》2022年第5期46-56,共11页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金青年科学基金资助项目(61802161);辽宁省自然科学基金资助项目(20180550886,2020-MS-292);辽宁省教育厅项目(JZL202015402);河北省自然科学基金青年科学基金资助项目(F2018203390)。

摘　　要：针对协同推荐算法面对托攻击时鲁棒性差的问题,提出一种融合随机森林和目标项目识别的鲁棒推荐算法。首先,基于卡方统计理论提取能够区分正常用户与攻击用户的有效特征。然后,训练随机森林分类器对攻击概貌进行第一阶段检测。接下来,通过识别目标项目对含有攻击概貌的类别做进一步检测,实现攻击概貌的第二阶段检测。最后,根据攻击概貌检测结果构建鲁棒推荐算法。实验结果表明,所提算法在保障推荐精度的前提下具有较强的鲁棒性。The collaborative recommendation algorithms have lower robustness in the presence of shilling attacks. To address this problem, a robust recommendation algorithm combining random forest and target item identification is proposed. Firstly, chi-square statistics is used to extract the effective features which can distinguish normal users and attacking users. Then, the random forest classifier is used to classify user profiles, which is the first stage of the attack profile detection. Next, the further detection to the cluster including attack profiles is performed by identifying the target item, which is the second stage of the attack profile detection. Finally, based on the detection results of attack profiles, the corresponding robust recommendation algorithm is constructed. Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

关 键 词：鲁棒推荐 随机森林 目标项目 卡方统计 攻击检测 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]