A Session-Based Recommendation Model That Integrates the Temporal Sequence of Session Interactions and the Global Distance-Awareness of Items with Graph Neural Networks

In the session-based recommendation algorithm, a better class of methods is to model the complex interaction relationship within the session as a graph structure, and then use the graph neural network to capture the deep features of the item from it. However, most models do not deeply mine the effective information contained in the sequence temporal relationship, nor do they pay attention to the auxiliary contribution of the global distance in different sessions to the item representation. This paper proposes a session-based recommendation model that integrates the temporal sequence of session interactions and the global distance-awareness of items with graph neural networks (TSDA-GNN). First, according to all session sequences, a global graph, a session graph, and a feature graph are constructed. In the global graph, the average "spacing" between items is introduced to represent the association between two items in the global graph. In the session graph, the interaction timing of the item is combined as the weight information in the relationship matrix, and the connection matrix of the session graph is constructed, which can more accurately capture the association relationship between items. Finally, by fusing the feature information of the item, using a graph neural network can deeply mine user preferences hidden in the feature information of the item. The model can not only model the information transmission relationship between items in different sessions, but also further deeply mine the hidden information of global domain and item features. Multiple experiments on two benchmark datasets show that the model proposed in this paper achieves the best results in various indicators.