Integrating Graph Neural Networks with Multi-Head Attention for Multi-Task Learning in Session-Based Recommendation

Session-based recommendation (SBR) endeavors to forecast the subsequent interaction item of an anonymous user, relying on their brief historical sequence of interactions. Due to the short-term nature of the session, the information available was limited. Most SBR algorithms mainly rely on current session information and ignore global context information. Therefore, previous approaches often suffer from data sparsity issues, leading to suboptimal performance. To this end, we propose a novel model, namely IGM-ML, which leverages global context as implicit feedback information via graph neural network, Adjustable weight parameters are then utilized to control global context information for recommendation tasks. Meanwhile, we design multi-head attention to learn the current explicit item transfer information to obtain local session-level representation. Given that the global implicit session representation and the local explicit session are learned using different approaches, we maximize the mutual information between them by introducing contrastive learning as an auxiliary task for the bridge. Then, We utilize multi-task learning approach to simultaneously optimize recommendation task and auxiliary task. IGM-ML has outperformed other state-of-the-art methods on three benchmark datasets, as evidenced by experimental results.