The pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.

中文翻译：

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推荐系统的可疑共同访问和共同评级行为以及异常取证的推断

个性化协作推荐系统的普遍性已显示出在各种电子商务服务（如Amazon，TripAdvisor，Yelp等）中的强大功能。然而，协作推荐系统的基本漏洞为恶意用户留下了影响推荐结果的空间，因为攻击者的愿望。现有的绝大多数检测方法都假定预先确定了恶意攻击的某些属性。实际上，由于具有挑战性的问题，提高检测性能通常受到限制：（a）各种类型的恶意攻击共存；（b）恶意攻击行为的有限表示；以及（c）在真实环境中探索和发现异常的实用证据世界数据稀缺。在本文中，我们以眼睛的方式研究了一个统一的检测框架，而不会受到攻击细节的困扰。首先，使用关联规则构造共同访问和共同评级图。然后，从链接模式，基于结构的属性和节点的固有关联的角度经验性地开发了节点的属性表示。最后，将属性信息和图的连接连贯性相结合，以推断可疑节点。对合成数据和真实数据的大量实验证明，与竞争基准相比，该检测方法的有效性。此外，异常取证指标包括评级意图的分布，可疑评级的时间汇总，