Collaborative filtering recommender system in adversarial environment

Hui Yu, Fei Zhang
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引用次数: 3

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.
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对抗环境下的协同过滤推荐系统
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。
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