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Knowledge-Enhanced Graph Transformer Network for Multi-Behavior and Item-Knowledge Session-based Recommendation

Session-based recommendations already play an important role in platforms such as e-commerce and streaming media, which are designed to predict the next interaction item based on a given session. Most of the current recommendation models only use the interaction sequence of the session to capture the potential conversion patterns between items, often ignore the user’s multi-type interaction behavior that reflects the user’s fine-grained preferences. At present, most models of multi-type interaction behaviors only learn user-item multi-type interaction behaviors and item-item dependencies relatively independently, and ignore the problems of item cold start and data sparsity. These issues motivate us to propose a new model MKGTN in this paper, we apply multi-type user-item interaction behaviors and item-item dependencies to session recommendation via MLP. Simultaneously, using a multi-task learning MLT paradigm involving learning knowledge embeddings as an auxiliary task to facilitate the main task of SR. Evaluations on three datasets show that MKGTN outperforms state-of-the-art multi-action interaction models, demonstrating the superiority of our model’s performance.