E-Commerce Recommendation Algorithm based on Graph Neural Network

Abstract

While recommender system is becoming an increasingly essential component in e-commerce websites, although previous models, which directly calculate the similarity of user/item history record, has obtained evidence of effectiveness, recommendations based solely on users' current sequence of actions, when user identity and history preference are not present, has been a popular area due to the growing privacy concerns. This paper demonstrates a model using a graph neural network, which takes the user's sequence of purchasing events as input and constructs a graph derived from it, to make the prediction of the most likely subsequent product that the customer may purchase and make personalized recommendations by the combination of session preference and user’s current interest. Experiments on a real-world e-commerce purchasing event dataset and analysis are carried out to test the model’s performance, as well as how the length of sequence may affect the model preference. The result shows that the model performance has attained a local peak on the dataset used.