Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi-criteria rating-based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power-item attack model, and introduce its four distinct variants adapted for multi-criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model-based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that the proposed method successfully detects attack profiles from genuine users even with a small selected size and attack size. The empirical outcomes also demonstrate that item popularity and user characteristics based on their rating profiles are highly beneficial features in capturing shilling attack profiles.

中文翻译：

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一种新颖的基于分类的多标准推荐系统先令攻击检测方法

推荐系统是一种新兴技术，通过考虑个人过去的评级行为来指导他们提供推荐。通过收集专注于区分项目视角的多标准偏好，作为传统推荐系统的新扩展，多标准推荐系统揭示了用户对某个项目的喜欢程度以及用户喜欢它的原因；因此，他们可以提高预测的准确性。然而，这些系统可能比传统系统更容易受到恶意攻击，因为它们暴露了用户对项目意见的多个维度。攻击者可能会尝试将虚假配置文件注入这些系统，以使推荐结果偏向于某些特定项目，或者使系统失去信誉。尽管存在多种方法来保护系统免受传统推荐系统的此类攻击，对于基于多标准评级的系统来说，通过捕获托儿资料来实现强大的系统仍然难以实现。因此，在本研究中，我们首先考虑一种突出且新颖的攻击类型，即能量项攻击模型，并介绍其适合多标准数据收集的四种不同变体。然后，我们提出了一种基于各种通用和基于模型的用户属性来检测托儿资料的分类方法，其中大多数是通常与项目流行度和评分值分布相关的新特征。在三个基准数据集上进行的实验得出的结论是，即使选择的大小和攻击规模较小，所提出的方法也能成功检测来自真实用户的攻击配置文件。