Mitigating Human and Computer Opinion Fraud via Contrastive Learning

2023/01/08 by Yuliya Tukmacheva, Ivan Oseledets, Tukmacheva, Yuliya +3
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection #Human-Computer Interaction (cs.HC) #Sentiment Analysis and Opinion Mining #Spam and Phishing Detection

paper · pdf · doi:10.48550/arxiv.2301.03025

openalex publication_date 2023/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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