Abstract:

Fake reviews affect online consumers’purchasing decisions. Efficiently identifying fake reviews is a pressing issue in the current development of e-commerce. Traditional methods for detecting fake reviews are often influenced by variations in review text style， syntax， and context， resulting in lower accuracy. Although large language models （LLMs） can address this accuracy issue， their training process is typically time-consuming. To tackle this problem， we propose a novel method called CF-DRI （cluster-based fine-tuning for deceptive review identification）. This method fine-tunes the pre-trained knowledge of LLMs by selecting clustered review samples， significantly enhancing the training efficiency for fake review identification. Compared to traditional methods， CF-DRI demonstrates superior performance with fewer fine-tuning samples. Experimental results on the Yelp.com dataset show that CF-DRI achieves a precision of 92.29% and a recall of 90.03% in fake review identification using only 20% of the clustered samples. This research provides new perspectives and solutions for managing fake reviews on e-commerce platforms， potentially promoting healthy industry development.

Key words: fake review identification, large language models, fine-tuning, clustering