## 4 Conclusions  
This paper proposes a graph neural network sequence recommendation model, TSDA-GNN, that integrates sequence interaction timing and global distance awareness to address the problems existing in current session-based recommendation algorithms. The model builds a global graph, a session graph, and a feature graph based on all the session sequences, and integrates complex interaction information, user preferences, and item feature information in the session sequences in the form of graphs, thus obtaining embeddings with different dimensions. In the global graph, the average \"distance\" between items is introduced to indicate the relevance of two items in the global domain. In the session graph, the interaction order of items is used as the weight information in the relation matrix to construct the connection matrix of the session graph, which can more accurately capture the relevance between items. Finally, by aggregating the feature vectors of item feature information in the feature graph, the graph neural network is used to deeply mine the hidden item preferences in the item feature information. The final session representation vector integrates node information of different levels and dimensions, fully mines the item transfer relationship of different levels, and can more effectively predict user interest preferences. The experiments show that the model performs best on all indicators. Although the experiments verify that this model has certain advantages, there are still some shortcomings, such as after constructing the connection matrix of the session graph, this paper uses a gated graph neural network to encode and learn the representation vectors of item representation vector and item feature information representation vector. This experiment proves that the gated graph neural network indeed has superiority in dealing with session data. However, the deeper networks such as gated graph neural networks have more complex structures, a longer model training time, and a higher demand for computing resources such as GPU. In the future, we need to find effective networks that can reduce model training times. Furthermore, we think that the vector representations of the session graph and the global graph are equally important in this paper. In fact, their roles or importance may be different. So, we will try to consider distinguishing the different importance of the global graph and the session graph in future research work. In addition, we have not considered using graph enhancement and contrastive learning to improve the performance of the model, which is what we also need to study in the future.  
Investigation, J.L.; methodology, [PERSON] and Y.W.; experiment, J.L.; supervision, Y.W. and G.S.; validation, J.L. and G.S.; writing--original draft, J.L.; writing--review and editing, Y.W. and N.Z.; data curation, N.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the science and technology project of State Grid Corporation of China (The key technologies and applications of brand influence evaluation and monitoring fits into the layout of \"One body and four wings\", grant no. 1400-202257240A-1-1-ZN) and the Fundamental Research Funds for the Central Universities (the project number is CUC23B008).  
Not applicable.  
Data Availability Statement: The data used to support the findings of this study are available from the corresponding author upon request. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.