Multi Global Information Assisted Streaming Session-Based Recommendation System

Streaming Session-Based Recommendation (SSBR) is a challenging problem as user preferences in sessions are continually drifting with sessions generated chronologically. In recent years, some SSBR models have been proposed to address this problem by reservoir technique and Graph Neural Networks (GNN) which help to preserve a representative sketch of the historical data and extract item transition information in sessions. However, there are two critical problems in existing methods: (1) most existing methods only focus on the local session information without exploiting the information of other sessions and users; (2) GNN models in existing SSBR methods are unable to capture the importance of different user features. To address the problems mentioned above, we propose a novel architecture named G lobal I tem and U ser embedding A ssisted G raph N eural N etwork ( GIUA-GNN ) for combining the global user and item information in an attentional manner with local session information for the recommendation. We also propose a novel architecture of graph neural network which utilizes the attention mechanism for better extracting the importance of different features of user embeddings named B i-directed A ttentional G raph C onvolutional N etwork ( BA-GCN ). Extensive experiments on three different sizes of real-world datasets have been conducted to demonstrate the superiority of our model on metrics MRR and Recall.