中国科学院大学学报2026，Vol.43Issue(2)：230-239,10.DOI:10.7523/j.ucas.2025.014
基于聚类微调的在线商品虚假评论识别
Fake review identification for online products based on clustering fine-tuning
摘要
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.关键词
虚假评论识别/大语言模型/微调/聚类Key words
fake review identification/large language models/fine-tuning/clustering分类
信息技术与安全科学引用本文复制引用
刘津浩,权沛,张文..基于聚类微调的在线商品虚假评论识别[J].中国科学院大学学报,2026,43(2):230-239,10.基金项目
国家自然科学基金(72174018,71932002)、北京市自然科学基金(9222001,9244021)和北京市教育委员会(SZ2021110005001)资助 （72174018,71932002）
