GCTN: Graph Competitive Transfer Network for Cross-Domain Multi-Behavior Prediction

Recently, the multi-behavior information on a specific domain has been successfully exploited by aggregating diverse user behaviors to solve the problems of cold start and data sparsity in recommendations. However, the user behavior information captured from multiple behaviors in a single domain is insufficient. Our study seeks to enhance user behavior prediction by leveraging both multi-behavior information and cross-domain information in a more effective manner. In order to explore the correlations and differences between different behaviors and different domains, we propose a novel competition framework consists of intra-domain competition and inter-domain competition for knowledge learning. Specifically, for intra-domain, a behavior competition mechanism is designed to enable the model to mine users’ interests and behavior patterns effectively. For inter-domain, a domain competition mechanism is designed to perform knowledge transfer and knowledge fusion for overlapping users in different domains. Through the competition mechanisms, our proposed Graph Competitive Transfer Network (GCTN) achieves knowledge transfer between different domains and captures users’ behavior patterns in different contexts. The effectiveness of the GCTN and its competition mechanisms has been validated through sufficient experimental trials on Douban and Amazon datasets. Compared to baseline methods, GCTN has demonstrated a marked improvement in both $AUC$ and $F1$ scores.