Fake Review Detection Based on Residual Networks Fusion of Multi-relationship Review Features
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.Cur-rent fake review detection methods based on graph neural networks(GNNs)are prone to network degradation and gradient disap-pearance during deep training.At the same time,review camouflage causes review markers to skew more,which affects the ro-bustness of GNNs detection model.To solve the above problems,a detection method based on residual network(MRDRN)is pro-posed,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 similari-ty 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 re-lationship 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.
Fake review detectionGraph neural networkResidual networkReview camouflageMulti-relationship features fu-sion
NETL
NSTL
万方数据
