## 3 Model Experiment
### Analysis of the Results  
We conducted comparative experiments on two datasets with the model proposed in this paper and the baseline models mentioned earlier. We used the statistical results of P@20 and MRR@20, and all the results are the average of 10 experimental results. The experimental results are shown in Table 2 below, from which it can be seen that the TSDA-GNN model proposed in this paper has the best performance on the tested indicators. The experimental results show that the POP model, which only considers the popularity of items themselves and recommends items with a high occurrence frequency, is no longer suitable for the current massive data environment, and has the worst performance among all models. The FPMC model, which uses a first-order Markov chain and matrix factorization method, shows relatively good results. The Item-KNN model, which uses the similarity between items to make recommendations, achieves better performance, but it does not consider learning the user's long-term interest representation based on their historical interaction behavior. The GRU4 Rec model, which uses a recurrent neural network to predict the items' behavior, is a good model for the current massive data environment. \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 POP & 1.17 & 0.28 & 1.98 & 0.89 \\ Item-KNN & 35.72 & 11.55 & 9.12 & 3.30 \\ FPMC & 20.22 & 6.63 & 16.02 & 7.29 \\ GRU4 Rec & 30.54 & 8.18 & 10.86 & 5.85 \\ NARM & 48.28 & 15.96 & 23.27 & 10.69 \\ STAMP & 46.52 & 15.13 & 26.42 & 13.35 \\ SR-GNN & 51.58 & 17.88 & 28.23 & 13.43 \\ GC-SAN & 42.85 & 13.56 & 12.25 & 6.06 \\ TA-GNN & 50.93 & 17.70 & 31.62 & 13.88 \\ GCE-GNN & 54.19 & 19.01 & 32.38 & 15.39 \\ TSDA-GNN & 55.21 & 19.45 & 33.29 & 15.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of experimental results on two datasets. Figure 8: Comparison of model performance on the Tmall dataset. Figure 7: Comparison of model performance on the Diginetica dataset. (RNN) to model all session data as a whole, learns the user's long-term interest preference, and uses a gated recurrent unit (GRU) to model user sequences, can capture the hidden association relationship in session sequences well. Compared with traditional methods, GRU4 Rec has greatly improved in performance indicators, which demonstrates that the session-based recommendation model based on deep learning is significantly better than traditional models. The NARM and STAMP models have further improved performance compared with GRU4 Rec. The NARM model produces better results due to the addition of an attention mechanism, which shows that the attention mechanism can effectively learn the user's main interest preference. The STAMP model also achieves superior performance by introducing the short-term interest of the session and combining it with the long-term interest, which leads to a better recommendation effect. The experimental results of models such as SR-GNN, GC-SAN, etc., are better than those using RNN, which shows that using graph neural network to capture the transformation relationship between session items and using attention mechanism to obtain user's interest preference is very effective in session recommendation. The excellent performance of the TA-GNN model shows that the target-aware attention learns different interest vector representations according to different target items, which improves the model's effect in personalized recommendation. In the biased personalized recommendation, the target-aware attention is better than the traditional attention mechanism, while the soft attention is more suitable for general recommendation.