## 4 Experiment and Analysis
### Results and Analysis  
#### 4.5.1 Comparative Experiment  
The experiment uses TensorFlow and PyTorch frameworks and uses Python language to simulate the existing nine baseline models and SE-GNNRM and compares and analyzes their performance. The comparison results are shown in Table 2.  
From Table 2, it can be found that the SE-GNNRM proposed in this paper achieves the best performance on the two evaluation metrics P@20 and MRR@20. On the Yoochoose1/64 data sets, it is 1.42% and 1.58% higher than the suboptimal algorithm, on the Diginetica data set, it is 1.53% and 0.1% higher than the second-best algor \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Yoochoose1/64**} & \multicolumn{2}{c}{**Diginetica**} & \multicolumn{2}{c}{**Tall**} \\ \cline{2-7}  & **P@20** & **MRR@20** & **P@20** & **MRR@20** & **P@20** & **MRR@20** \\ \hline FPMC & 45.62 & 15.01 & 26.53 & 6.95 & 16.06 & 7.32 \\ Item-KNN & 51.60 & 21.81 & 35.75 & 11.57 & 9.15 & 3.31 \\ \hline GRU4 REC & 60.64 & 22.89 & 30.79 & 15.46 & 10.83 & 5.89 \\ NARM & 68.32 & 28.63 & 48.43 & 16.11 & 23.30 & 10.70 \\ CSRM & 71.45 & 30.36 & 50.55 & 16.38 & 29.46 & 13.96 \\ \hline SR-GNN & 70.57 & 30.94 & 50.73 & 17.59 & 27.57 & 13.72 \\ FGNN & 71.12 & 31.68 & 51.36 & 18.47 & 25.24 & 10.39 \\ \hline STAMP & 68.74 & 28.67 & 46.62 & 15.13 & 26.47 & 13.36 \\ CoSAN & 69.71 & 29.92 & 48.34 & 15.22 & 27.21 & 13.12 \\ \hline SE-GNNRM & 72.76 & 32.18 & 52.84 & 18.49 & 32.70 & 15.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative experimental results. data set, it is 10.9% and 11.8% higher than the second-best algorithm. The SE-GNNRM effectively demonstrates its ability to recommend all three data sets. In the early recommendation models, the FPMC model limits dependencies in the session sequence, so the recommendation effect is poor. Item-KNN outperforms [PERSON] chain-based algorithms, but Item-KNN only recommends highly similar items and does not fully consider the problem of order transitions in sessions. Compared with traditional algorithms, deep learning-based models explicitly model the user's global behavioral preferences and take into account the transition between the user's previous behavior and the next click, effectively proving their ability to capture session sequences. There is a stronger ability to sequentially transform relationships. GRU4 REC, a classic work on modeling session sequences using RNNs, proves that encoding the current session with GRU units is more suitable for session recommendation problems than traditional RNN and LSTM on session recommendation problems and takes the last layer as a general preference for sessions. However, the interest preferences represented by session embeddings are variable, and GRU4 REC does not distinguish current preferences from general preferences. On the basis of GRU4 REC, NARM solves this problem well. After using the GRU unit to encode the current session to obtain the item embedding, it dynamically selects and linearly combines different parts of the input session sequence through the soft attention mechanism. The experimental results also demonstrate the effectiveness of this mechanism for learning session preferences. CSRM also uses a GRU-based RNN to model the session sequence while also considering the current and general preferences of the session. Furthermore, CSRM designs an external memory encoding layer for learning the collaborative information of adjacent sessions, which makes it have better recommendation performance than many models modeling a single session. In the comparative experimental results, the GNN-based algorithm is more competitive than the RNN-based algorithm. SR-GNN further considers the dependencies of item transitions in a session by modeling each session as a directed graph. The items in the session are the nodes in the graph, which can capture more complex and implicit dependencies within the session.