基于用户属性的推荐系统攻击检测快速准确

Mehmet Aktukmak, Y. Yilmaz, Ismail Uysal
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引用次数: 21

摘要

恶意配置文件已经成为协作推荐系统的可靠威胁。攻击者提供虚假的物品评级来系统地操纵平台。攻击检测算法可以通过观察评级分布来识别和删除此类用户。在本研究中,我们的目标是使用用户属性作为附加信息源,以提高攻击检测的准确性和速度。我们提出了一种概率分解模型,该模型可以将混合数据类型的用户属性和观察到的评级嵌入到潜在空间中,以生成新用户的异常统计。为了识别系统中的持久异常值,我们还提出了一种基于从真实用户那里学习的概率模型的顺序攻击检测算法,以实现快速准确的检测。与流行的基准数据集上的基线算法相比,所提出的模型在准确性和速度方面都有显着提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quick and accurate attack detection in recommender systems through user attributes
Malicious profiles have been a credible threat to collaborative recommender systems. Attackers provide fake item ratings to systematically manipulate the platform. Attack detection algorithms can identify and remove such users by observing rating distributions. In this study, we aim to use the user attributes as an additional information source to improve the accuracy and speed of attack detection. We propose a probabilistic factorization model which can embed mixed data type user attributes and observed ratings into a latent space to generate anomaly statistics for new users. To identify the persistent outliers in the system, we also propose a sequential attack detection algorithm to enable quick and accurate detection based on the probabilistic model learned from genuine users. The proposed model demonstrates significant improvements in both accuracy and speed when compared to baseline algorithms on a popular benchmark dataset.
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