Mitigating Human and Computer Opinion Fraud via Contrastive Learning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 8 Jan 2023.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2020The Pile: An 800GB Dataset of Diverse Text for Language Modelingcited by 2,346
- 2019Hello, It's GPT-2 -- How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systemscited by 110
- 2017Translation-based Recommendationcited by 70
- 2020Revisiting Adversarially Learned Injection Attacks Against Recommender Systemscited by 30
- 2020Attacking Recommender Systems with Augmented User Profilescited by 24
- 2020Robust Spammer Detection by Nash Reinforcement Learningcited by 17
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