基于GCN的统一鲁棒推荐和欺诈检测的用户表示学习
GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection
摘要：
近年来，推荐系统已经成为所有电子商务平台中不可或缺的功能。推荐系统的评论评级数据通常来自开放平台，这可能会吸引一群恶意用户故意插入虚假反馈，试图使推荐系统偏向他们。这种攻击的存在可能会违反建模假设，即高质量的数据总是可用的，并且这些数据真实地反映了用户的兴趣和偏好。因此，构建一个鲁棒的推荐系统，即使在存在先令攻击的情况下也能产生稳定的推荐，具有重要的现实意义。在本文中，我们提出了GraphRfi -一个基于GCN的用户表示学习框架，以统一的方式执行鲁棒的推荐和欺诈者检测。在其端到端学习过程中，欺诈者检测组件中用户被识别为欺诈者的概率自动确定该用户的评级数据在推荐组件中的贡献;而推荐组件中输出的预测误差则作为欺诈者检测组件中的重要特征。因此，这两个组成部分可以相互促进。大量的实验已经进行，实验结果表明，我们的GraphRfi在两个任务中的优越性-鲁棒的评级预测和欺诈者检测。此外，所提出的GraphRfi被验证为更强大的各种类型的先令攻击的国家的最先进的推荐系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
