通过对比学习减轻人类和计算机的意见欺诈
Mitigating Human and Computer Opinion Fraud via Contrastive Learning
摘要：
我们介绍了新的方法对虚假文本评论检测协同过滤推荐系统。现有的算法集中于检测由语言模型生成的虚假评论，而忽略了由不诚实的用户撰写的文本，主要是为了金钱利益。我们提出了基于对比学习的架构，它利用用户的人口统计特征，以及文本评论，作为反对假货的额外证据。通过这种方式，我们能够解释两种不同类型的虚假评论垃圾邮件，并使推荐系统对有偏见的评论更加强大。
英文摘要：
We introduce the novel approach towards fake text reviews detection in collaborative filtering recommender systems. The existing algorithms concentrate on detecting the fake reviews, generated by language models and ignore the texts, written by dishonest users, mostly for monetary gains. We propose the contrastive learning-based architecture, which utilizes the user demographic characteristics, along with the text reviews, as the additional evidence against fakes. This way, we are able to account for two different types of fake reviews spamming and make the recommendation system more robust to biased reviews.
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。
