Graph Agent Transformer Network With Contrast Learning for Cross-Domain Recommendation of E-Commerce

Abstract

Cross-domain recommendation(CDR) system is a kind of recommendation system based on multiple data fields, its core idea is to integrate data from different fields, so as to provide users with more accurate and personalized recommendation services. This paper constructs a hybrid contrast learning graph agent transformer network for CDR of e-commerce. In this method, data in target domain and source domain are first constructed into a multi-part graph structure, and then data mining is carried out by double-branch graph agent transformer. The final embedding of each node in the graph is obtained by training the model through mixed supervised learning and comparative learning. This method not only increases the recommended performance of the model through comparative learning, but also improves the transformer structure through agent and reduces the calculation cost of the model. Simulation results on two real cross-domain recommendation datasets of e-commerce demonstrate the effectiveness of the proposed method.