对抗环境下的协同过滤推荐系统

Hui Yu, Fei Zhang
{"title":"对抗环境下的协同过滤推荐系统","authors":"Hui Yu, Fei Zhang","doi":"10.1109/ICMLC.2012.6358947","DOIUrl":null,"url":null,"abstract":"Collaborative filtering recommender system is wildly used in e-commerce system. According to the profiles of user or items, a collaborative filtering recommender system recommends items to targeted customers according to the preferences of their similar customers. It provides customer useful relevant information. Unfortunately, the recommender system is vulnerable to profile injection attacks. In the profile inject attack, the similar user profiles are manipulated by injecting a large number of fake profiles into the system. In this paper, four new attributes for the injection attack detection are proposed. We also discuss the profile injection attacks in adversarial learning environment. By applying the Localized Generalization Error Model (L-GEM), a more robustness attack profile detection system is proposed. Experimental results show that L-GEM based detection classifier has better robustness.","PeriodicalId":128006,"journal":{"name":"2012 International Conference on Machine Learning and Cybernetics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Collaborative filtering recommender system in adversarial environment\",\"authors\":\"Hui Yu, Fei Zhang\",\"doi\":\"10.1109/ICMLC.2012.6358947\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Collaborative filtering recommender system is wildly used in e-commerce system. According to the profiles of user or items, a collaborative filtering recommender system recommends items to targeted customers according to the preferences of their similar customers. It provides customer useful relevant information. Unfortunately, the recommender system is vulnerable to profile injection attacks. In the profile inject attack, the similar user profiles are manipulated by injecting a large number of fake profiles into the system. In this paper, four new attributes for the injection attack detection are proposed. We also discuss the profile injection attacks in adversarial learning environment. By applying the Localized Generalization Error Model (L-GEM), a more robustness attack profile detection system is proposed. Experimental results show that L-GEM based detection classifier has better robustness.\",\"PeriodicalId\":128006,\"journal\":{\"name\":\"2012 International Conference on Machine Learning and Cybernetics\",\"volume\":\"21 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2012.6358947\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2012.6358947","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

协同过滤推荐系统在电子商务系统中有着广泛的应用。协同过滤推荐系统根据用户或商品的个人资料,根据目标顾客的相似偏好向目标顾客推荐商品。为客户提供有用的相关信息。不幸的是,推荐系统很容易受到配置文件注入攻击。在配置文件注入攻击中,通过向系统中注入大量的假配置文件来操纵相似的用户配置文件。本文提出了用于注入攻击检测的四个新属性。讨论了对抗性学习环境下的配置文件注入攻击。应用局部泛化误差模型(L-GEM),提出了一种鲁棒性更强的攻击轮廓检测系统。实验结果表明,基于L-GEM的检测分类器具有较好的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative filtering recommender system in adversarial environment
Collaborative filtering recommender system is wildly used in e-commerce system. According to the profiles of user or items, a collaborative filtering recommender system recommends items to targeted customers according to the preferences of their similar customers. It provides customer useful relevant information. Unfortunately, the recommender system is vulnerable to profile injection attacks. In the profile inject attack, the similar user profiles are manipulated by injecting a large number of fake profiles into the system. In this paper, four new attributes for the injection attack detection are proposed. We also discuss the profile injection attacks in adversarial learning environment. By applying the Localized Generalization Error Model (L-GEM), a more robustness attack profile detection system is proposed. Experimental results show that L-GEM based detection classifier has better robustness.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信