A social commerce information propagation prediction model based on transformer

Abstract

Precise forecasts of the propagation patterns of social commerce information play a crucial part in precision marketing. Traditional forecast relies on machine learning diffusion models, in which the forecast accuracy is dependent on the quality of the designed features. Researchers using these models are required to have experience in this regard, but due to the complexity and variations of real-world social commerce information propagation, design of features for the prediction model turns out difficult and is likely to cause local or universal errors in the model. To address these problems, this study proposed an information propagation prediction model based on Transformer. First, the fully-connected neural network was employed to code the user nodes to low-dimension vectors; then, Transformer was employed to perform information of the user-node vectors; last, the output of the Transformer was uploaded to the output layer to forecast the next user node in information propagation. The model was tested on data sets obtained from Sina Weibo, and the test result shows that the proposed model outperformed baseline models in terms of the indicators of Acc@k and MRR.