Collaborative filtering recommender systems (CFRSs) have already been proved effective to cope with the information overload problem since they merged in the past two decades. However, CFRSs are highly vulnerable to shilling or profile injection attacks since their openness. Ratings injected by malicious users seriously affect the authenticity of the recommendations as well as users’ trustiness in the recommendation systems. In the past two decades, various studies have been conducted to scrutinize different profile injection attack strategies, shilling attack detection schemes, robust recommendation algorithms, and to evaluate them with respect to accuracy and robustness. Due to their popularity and importance, we survey about shilling attacks in CFRSs. We first briefly discuss the related survey papers about shilling attacks and analyze their deficiencies to illustrate the necessity of this paper. Next we give an overall picture about various shilling attack types and their deployment modes. Then we explain profile injection attack strategies, shilling attack detection schemes and robust recommendation algorithms proposed so far in detail. Moreover, we briefly explain evaluation metrics of the proposed schemes. Last, we discuss some research directions to improve shilling attack detection rates robustness of collaborative recommendation, and conclude this paper.

中文翻译：

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针对协作推荐系统的先令攻击：综述

协同过滤推荐系统（CFRS）自过去二十年合并以来已被证明可以有效应对信息过载问题。然而，由于 CFRS 的开放性，它们极易受到先令或配置文件注入攻击。恶意用户注入的评分严重影响推荐的真实性以及用户对推荐系统的信任度。在过去的 20 年中，已经进行了各种研究来审查不同的配置文件注入攻击策略、先令攻击检测方案、稳健的推荐算法，并在准确性和稳健性方面评估它们。由于它们的受欢迎程度和重要性，我们调查了 CFRS 中的先令攻击。我们首先简要讨论有关先令攻击的相关调查论文，并分析其不足之处，以说明本文的必要性。接下来我们全面介绍各种先令攻击类型及其部署模式。然后我们详细解释了迄今为止提出的配置文件注入攻击策略、先令攻击检测方案和鲁棒推荐算法。此外，我们简要解释了所提出方案的评估指标。最后，我们讨论了一些研究方向，以提高协同推荐的先令攻击检测率的鲁棒性，并总结本文。到目前为止详细提出的先令攻击检测方案和鲁棒推荐算法。此外，我们简要解释了所提出方案的评估指标。最后，我们讨论了一些研究方向，以提高协同推荐的先令攻击检测率的鲁棒性，并总结本文。到目前为止详细提出的先令攻击检测方案和鲁棒推荐算法。此外，我们简要解释了所提出方案的评估指标。最后，我们讨论了一些研究方向，以提高协同推荐的先令攻击检测率的鲁棒性，并总结本文。