Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (4): 314-323.doi: 10.11896/jsjkx.230200020

• Artificial Intelligence • Previous Articles     Next Articles

Fake Review Detection Based on Residual Networks Fusion of Multi-relationship Review Features

LUO Zeyang1, TIAN Hua1, DOU Yingtong2, LI Manwen1, ZHANG Zehua1   

  1. 1 College of Information and Computer,Taiyuan University of Technology,Jinzhong,Shanxi 030600,China
    2 University of Illinois at Chicago,Chicago 60607,USA
  • Received:2023-02-05 Revised:2023-06-26 Online:2024-04-15 Published:2024-04-10
  • Supported by:
    National Natural Science Foundation of China(61702356,51901152),Industry University Cooperation Education Program of the Ministry of Education(2020021680113) and Shanxi Scholarship Council of China(2020-040).

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Abstract: With the rise of e-commerce and short video community platforms,the emergence of fake reviews has seriously affected user experience.Even to combat platform detection,review camouflage makes it harder to distinguish between true and false.Current fake review detection methods based on graph neural networks(GNNs) are prone to network degradation and gradient disappearance during deep training.At the same time,review camouflage causes review markers to skew more,which affects the robustness of GNNs detection model.To solve the above problems,a detection method based on residual network(MRDRN) is proposed,which can fuse the features of multi-relationship reviews to identify fake reviews.Firstly,in order to slow down network degradation,the feature extraction of deep reviews is carried out by combining residual network.A new neighbor mixed sampling strategy is proposed,which can be used to conduct low-and high-order neighbor mixed sampling according to the feature similarity between reviews,so as to alleviate the problem of imbalanced review marks and learn more rich review features.Secondly,a multi-relationship review features fusion strategy is proposed,which reduces the impact of review masking by integrating intra relationship review network topology and inter relationship review features as a whole.Experimental results on three real datasets show that MRDRN has higher detection capability and stronger robustness than the standard method.

Key words: Fake review detection, Graph neural network, Residual network, Review camouflage, Multi-relationship features fusion

CLC Number: 

  • TP389.1
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