## 3 Model Experiment
### Ablation Experiments  
We conducted comparative experiments to verify whether the main components of the model are effective. We designed the following variant models for experiments:  
(1) The TSDA-GNN-A model means that only the average \"distance\" between items is introduced in the global graph, and the feature information embedding vector and session-level item embedding vector of the item are fused to obtain the final global domain layer item embedding vector.  
(2) The TSDA-GNN-B model means that only in the process of constructing the item connection matrix of the session graph is the interaction order information encoded to obtain the initial vector of the session node, and that the feature information embedding vector and session layer embedding vector of the item are fused to obtain the final session layer item embedding vector.  
(3) The TSDA-GNN-F model means that in the process of learning the item embedding vectors of the session layer and the global domain layer, the fusion of item feature information is not considered. The results of the ablation experiments are shown as the Table 3:  
The table above shows the experimental results of different comparison models. From the results, we can see that when the average \"distance\" between items in the global graph and the interaction order of items in the session graph are not considered as weight information in the relation matrix to construct the item connection matrix, the corresponding indicator values in the experimental results will decrease. Figures 9 and 10 more vividly show the performance of each variant model on the dataset.  
The comparison results above show that the TSDA-GNN-A model, in the process of calculating the session layer item embedding vector, calculates the adjacency matrix according to the directed session graph of the session data. We used the same method as in GC-SAN and other graph neural network-based recommendation model algorithms to construct it, and its experimental results have decreased significantly. This shows that using the interaction order of items in the session graph as weight information in the relation matrix to construct the item connection matrix can more effectively learn the association relationship between items.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**Diginetica**} & \multicolumn{2}{c}{**Tmall**} \\ \hline
**Metrics** & **P@20** & **MRR@20** & **P@20** & **MRR@20** \\ \hline TSDA-GNN-A & 53.76 & 18.41 & 32.86 & 15.32 \\ TSDA-GNN-B & 54.86 & 19.24 & 32.82 & 15.39 \\ TSDA-GNN-F & 54.53 & 19.04 & 32.71 & 15.21 \\ TSDA-GNN & 55.21 & 19.45 & 33.29 & 15.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments.  
Figure 10: Results of ablation experiments on the Tmall dataset.  
Figure 9: Results of ablation experiments on the Diginetica dataset.  
The TSDA-GNN-B model, in the process of calculating the global domain layer item embedding vector, uses only the frequency of each edge in all sessions to represent the weight of adjacent edges in the domain. Its experimental results also have a certain decline, which shows that our combination of the average \"distance\" between items to characterize the association strength of two items has a certain auxiliary effect on learning the global domain layer item embedding representation.  
According to the comparison results of TSDA-GNN-F and TSDA-GNN on two datasets, fusing item feature information plays a certain role in accurately learning user preferences. On Diginetica, compared with TSDA-GNN-F, TSDA-GNN's value increased by 1.2 percentage points on P@20 and 2.1 percentage points on MRR@20. On the Tmall dataset, compared with TSDA-GNN-F, TSDA-GNN's value increased by 1.7% and 3.8% on P@20 and MRR@20, respectively.