计算机科学 ›› 2026, Vol. 53 ›› Issue (4): 188-196.doi: 10.11896/jsjkx.250500088
辜波凯, 刘盾, 孙扬
GU Bokai, LIU Dun, SUN Yang
摘要： 随着在线评论对消费者决策的影响日益增强,虚假评论的检测成为保障电商平台生态健康的重要任务。现有方法多采用静态单步检测,忽视了动态特征与决策成本,导致检测效率不佳。为此,提出一种基于序贯三支决策(Sequential Three-Way Decisions)与深度学习(Deep Learning)的多粒度虚假评论检测(Fake Review Detection)方法(STWD-DLFRD)。该框架通过深度学习技术提取评论的文本、行为及社交关系特征,构建多粒度特征空间,并利用序贯三支的分层决策机制实现对不同复杂度虚假评论的动态检测。实验结果表明,与基线模型相比,STWD-DLFRD在F1值和准确率上表现最优,总分类代价显著降低。所提方法为动态环境下高成本敏感的虚假评论检测提供了一种有效的解决方案。
中图分类号:
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