## 4 Experiments
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Diginetica**} & \multicolumn{2}{c}{**Yoochoose 1/64**} & \multicolumn{2}{c}{**Yoochoose 1/4**} \\  & **P@20** & **MRR@20** & **P@20** & **MRR@20** & **P@20** & **MRR@20** \\ \hline Item-KNN & 35.75 & 11.57 & 51.60 & 21.81 & 52.31 & 21.70 \\ FPMC & 26.53 & 6.95 & 45.62 & 15.01 & - & - \\ GRU4 Rec & 29.45 & 8.33 & 60.64 & 22.89 & 59.53 & 22.60 \\ NARM & 49.70 & 16.17 & 68.32 & 28.63 & 69.73 & 29.23 \\ STAMP & 45.64 & 14.32 & 68.74 & 29.67 & 70.44 & 30.00 \\ SASRec & 52.97 & 18.43 & 68.39 & 29.26 & 68.27 & 29.22 \\ SR-GNN & 50.73 & 17.59 & 70.57 & 30.94 & 71.36 & 31.89 \\ GC-SAN & 52.33 & 18.37 & 69.49 & 30.25 & 70.37 & 31.05 \\ GCE-GNN & 54.22 & 19.04 & 70.90 & 31.26 & 71.40 & 31.49 \\ DHCN & 53.18 & 18.44 & 70.74 & 31.05 & 71.58 & 31.72 \\ MTD & 53.18 & 18.33 & 71.12 & 31.28 & 71.83 & 30.83 \\ ICM-SR & 52.28 & 17.63 & 71.11 & 31.23 & - & - \\ CGL & 52.11 & 18.64 & 69.32 & 29.96 & 70.05 & 31.13 \\ DGS-MGNN & 54.13 & 19.02 & 72.62 & 32.49 & 73.10 & 33.55 \\ LDGC-SR & 54.38 & 18.96 & - & - & - & - \\ GCAN & 54.73 & 19.69 & - & - & - & - \\ CSRM & 49.63 & 16.47 & 69.20 & 29.18 & 69.61 & 29.64 \\ FGNN & 51.21 & 17.21 & 71.03 & 30.74 & 71.49 & 31.08 \\ CTA & 50.51 & 17.19 & 70.25 & 30.65 & 71.25 & 31.11 \\ SR-IEM & 50.35 & 17.06 & 70.03 & 30.16 & 70.45 & 30.11 \\ HG-GNN & 49.88 & 16.29 & 69.31 & 29.02 & 70.37 & 30.86 \\ IGT & 40.13 & 17.94 & 71.82 & 31.35 & 72.13 & 31.93 \\
**SkipGT (Ours)** & **55.72** & **20.69** & **76.78** & **34.59** & **77.32** & **35.29** \\
**Improv. (\%)** & **1.8** & **5.1** & **5.73** & **6.46** & **5.77** & **5.19** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the proposed SkipGT with the baseline methods. superior performance compared to NARM across all datasets. SR-GNN aggregates click sequences into a graph, enabling the capture of richer item transformation information, thereby highlighting the effectiveness of graph neural networks (GNNs) in session-based recommendation. GC-SAN builds upon SR-GNN by incorporating the weight advantage of its attention mechanism, leading to further enhanced recommendations. The performance of CTA is statistically indistinguishable from GNN-based methods across all datasets, demonstrating CTA's ability to effectively combine temporal and contextual information. IGT surpasses SR-GNN, showcasing the significant benefit of incorporating time intervals for superior next-click prediction. Furthermore, IGT outperforms CTA to a certain degree, underlining the importance of its interval-enhanced graph transformer module in capturing user behavior patterns. GCAN achieves the best performance among all methods on the DIGINETICA dataset, emphasizing the value of a collaborative approach for modeling item representations across sessions, allowing it to engage users with relevant items within specific subsequences. GNN-based baselines: In contrast to RNN-based approaches, SR-GNN, FGNN, and GCSAN leverage graph neural networks (GNNs) to aggregate session sequences into graph structures. This enables the capture of intricate transformation information among items, leading to enhanced recommendation performance. However, experimental results demonstrate that simply stacking GNNs and self-attention networks (SANs) to capture user features does not yield significant performance improvements compared to SR-GNN. Algorithms that incorporate collaborative information, such as GCE-GNN, MTD, DHCN, ICM-SR, CGL, DGS-MGNN, and LDGC-SR, exhibit notably better performance than SR-GNN. This finding suggests that information from other sessions can enrich the current session's data, consequently improving recommendation accuracy. Notably, GCE-GNN outperforms DHCN, ICM-SR, and CGL, highlighting the potential negative impact of introducing irrelevant item embeddings into the current session's recommendation process. Furthermore, LDGC-SR and MTD achieve superior results compared to GCE-GNN.