A Fake Reviews Detection Method Based on Improved Graph Neural Network
Fake reviews in e-commerce platforms mislead consumers'purchase decisions,and damage the rights and interests of consumers and legitimate businesses.The existing methods are difficult to find the implicit association between fake reviews.In order to improve the clas-sification accuracy of fake reviews detection,a fake review detection method based on the TrustRank algorithm and graph neural network was proposed.Firstly,the features associated with the fake reviews were constructed,and the importance scores of these features were calculated.Secondly,to improve the random sampling algorithm of GraphSAGE,the suspicious values of fake reviews were calculated by the improved TrustRank method,which combined the adaptive neighborhood sampling strategy was used to select nodes in the graph with bias and adaptive and generate the neighborhood of target nodes.Finally,Yelp data set was used to verify the proposed model.The accuracy,recall and F1 of TR-GraphSAGE model were approximately 5.86％,15.01％,and 10.12％better on average,respectively,than LSTM,TextCNN,GCN,and GraphSAGE.The TR-GraphSAGE model can eliminate the noise that affects the prediction,ensure the quality and quantity of the neighbor-hood,and thus improve the quality of the associated feature representation,which provides a new method for fake review detection.
万方数据
维普
