Considering the premise that the number of products offered grow in an
exponential fashion and the amount of data that a user can assimilate before
making a decision is relatively small, recommender systems help in categorizing
content according to user preferences. Collaborative filtering is a widely used
method for computing recommendations due to its good performance. But, this
method makes the system vulnerable to attacks which try to bias the
recommendations. These attacks, known as 'shilling attacks' are performed to
push an item or nuke an item in the system. This paper proposes an algorithm to
detect such shilling profiles in the system accurately and also study the
effects of such profiles on the recommendations.

该论文提出了一种准确检测系统中有假评价账户的算法，并研究这些账户对推荐系统的影响。

通过缓解操纵攻击来推进推荐系统

Advancing Recommender Systems by mitigating Shilling attacks

Review-Based Recommender Systems (RBRS) have attracted increasing research
interest due to their ability to alleviate well-known cold-start problems. RBRS
utilizes reviews to construct the user and items representations. However, in
this paper, we argue that such a reliance on reviews may instead expose systems
to the risk of being shilled. To explore this possibility, in this paper, we
propose the first generation-based model for shilling attacks against RBRSs.
Specifically, we learn a fake review generator through reinforcement learning,
which maliciously promotes items by forcing prediction shifts after adding
generated reviews to the system. By introducing the auxiliary rewards to
increase text fluency and diversity with the aid of pre-trained language models
and aspect predictors, the generated reviews can be effective for shilling with
high fidelity. Experimental results demonstrate that the proposed framework can
successfully attack three different kinds of RBRSs on the Amazon corpus with
three domains and Yelp corpus. Furthermore, human studies also show that the
generated reviews are fluent and informative. Finally, equipped with Attack
Review Generators (ARGs), RBRSs with adversarial training are much more robust
to malicious reviews.

通过提出基于生成器的模型，利用强化学习生成伪造的评论攻击基于评论的推荐系统，实现狡猾的物品推广，并通过引入辅助奖励和预先训练的语言模型和方面预测器来提高文本流畅性和多样性。然后，经过实验证明，具有对抗性训练的推荐系统能够防止这种攻击。

通过伪造评论生成来干扰黑盒基于评论的推荐系统

Shilling Black-box Review-based Recommender Systems through Fake Review  Generation

In shilling attacks, an adversarial party injects a few fake user profiles
into a Recommender System (RS) so that the target item can be promoted or
demoted. Although much effort has been devoted to developing shilling attack
methods, we find that existing approaches are still far from practical. In this
paper, we analyze the properties a practical shilling attack method should have
and propose a new concept of Cross-system Attack. With the idea of Cross-system
Attack, we design a Practical Cross-system Shilling Attack (PC-Attack)
framework that requires little information about the victim RS model and the
target RS data for conducting attacks. PC-Attack is trained to capture graph
topology knowledge from public RS data in a self-supervised manner. Then, it is
fine-tuned on a small portion of target data that is easy to access to
construct fake profiles. Extensive experiments have demonstrated the
superiority of PC-Attack over state-of-the-art baselines. Our implementation of
PC-Attack is available at this https URL.

本研究分析了炒信攻击方法的实际应用问题，提出了跨系统攻击的概念并设计了一个新的实用跨系统炒信攻击框架，使用图拓扑知识进行自我监督的训练，在很少了解受害者 RS 模型和目标 RS 数据情况下构建虚假档案，具有比现有基线的卓越性能。

具有受限数据访问的实用跨系统水军攻击

Practical Cross-System Shilling Attacks with Limited Access to Data

In recent years, recommender system has become an indispensable function in
all e-commerce platforms. The review rating data for a recommender system
typically comes from open platforms, which may attract a group of malicious
users to deliberately insert fake feedback in an attempt to bias the
recommender system to their favour. The presence of such attacks may violate
modeling assumptions that high-quality data is always available and these data
truly reflect users' interests and preferences. Therefore, it is of great
practical significance to construct a robust recommender system that is able to
generate stable recommendations even in the presence of shilling attacks. In
this paper, we propose GraphRfi - a GCN-based user representation learning
framework to perform robust recommendation and fraudster detection in a unified
way. In its end-to-end learning process, the probability of a user being
identified as a fraudster in the fraudster detection component automatically
determines the contribution of this user's rating data in the recommendation
component; while the prediction error outputted in the recommendation component
acts as an important feature in the fraudster detection component. Thus, these
two components can mutually enhance each other. Extensive experiments have been
conducted and the experimental results show the superiority of our GraphRfi in
the two tasks - robust rating prediction and fraudster detection. Furthermore,
the proposed GraphRfi is validated to be more robust to the various types of
shilling attacks over the state-of-the-art recommender systems.

本文提出了一种基于 GraphRfi 的 GCN 用户表示学习框架，以实现强健的建议和舞弊者检测，此框架在强健的评级预测和舞弊者检测这两个任务中表现更为优越，并且证实其比现有的推荐系统更具鲁棒性。