Mitigating Human and Computer Opinion Fraud via Contrastive Learning

• URL: http://arxiv.org/abs/2301.03025v1
• Date: Sun, 8 Jan 2023 12:02:28 GMT
• Title: Mitigating Human and Computer Opinion Fraud via Contrastive Learning
• Authors: Yuliya Tukmacheva, Ivan Oseledets, Evgeny Frolov
• Abstract summary: 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. We propose the contrastive learning-based architecture, which utilizes the user demographic characteristics, along with the text reviews, as the additional evidence against fakes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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