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首页> 外文期刊>International journal of digital crime and forensics >Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems
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Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems

机译：在协同过滤推荐系统中检测异常等级

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摘要

Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to "shilling" attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.
机译：在线评级数据在现有的流行电子商务网站（如Amazon，Yelp等）上无处不在，这深刻影响了以下客户对电子商务商人使用产品的选择。协作过滤推荐系统（CFRS）在评级系统中起着至关重要的作用。由于CFRS极易遭受“先令”攻击，因此攻击者经常会以恶意汇率污染评级系统，从而达到其攻击意图。尽管基于这种攻击的检测方法受到了广泛关注，但是检测精度的问题仍未解决。此外，几乎没有人可以扩展以处理大型网络。本文提出了一种快速有效的检测方法，该方法将两个阶段结合起来以找出异常用户。首先，手稿采用图挖掘方法，在具有数百万个节点的构造图中自动发现可疑节点。然后，根据第一阶段的结果，该手稿将继续利用可疑目标项目来确定异常用户。实验评估了该方法的有效性。

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