# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 5 Experiments
### Overall Performance(RQ1)  
Table 3 shows the overall performance of DEISI compared with the baseline models. We take the average of 10 runs as the result. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistics** & **Nowplaying** & **Yoochoose1/64** & **Diginetica** \\ \hline interactions & 1,367,963 & 557,248 & 982,961 \\ training sess & 825,304 & 369,859 & 719,470 \\ test sess & 89,824 & 55,898 & 60,858 \\ items & 60,417 & 16,766 & 43,097 \\ avg. length & 7.42 & 6.16 & 5.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results of datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Nowplaying**} & \multicolumn{5}{c}{**Yoochoose1/64**} & \multicolumn{5}{c}{**Diginetica**} \\ \cline{2-13}  & **P@10** & **M@10** & **P@20** & **M@20** & **P@10** & **M@10** & **P@20** & **M@20** & **P@10** & **M@10** & **P@20** & **M@20** \\ \hline FPMC & 0.0601 & 0.0259 & 0.0736 & 0.0282 & 0.3015 & 0.0972 & 0,4562 & 0.1501 & 0.1539 & 0.0619 & 0.2639 & 0.0689 \\ NARM & 0.1725 & 0.0601 & 0.1854 & 0.0693 & 0.5920 & 0.2495 & 0.6811 & 0.2855 & 0.3544 & 0.1513 & 0.4970 & 0.1618 \\ GRU4 Rec & 0.0632 & 0.0415 & 0.0792 & 0.0449 & 0.5011 & 0.1789 & 0.6063 & 0.2288 & 0.1789 & 0.0730 & 0.2939 & 0.0829 \\ STAMP & 0.1590 & 0.0531 & 0.1766 & 0.0688 & 0.6190 & 0.2583 & 0.6874 & 0.2967 & 0.3291 & 0.1378 & 0.4539 & 0.1429 \\ \hline SR-GNN & 0.1533 & 0.0607 & 0.1776 & 0.0749 & 0.6197 & 0.2651 & 0.7055 & 0.3094 & 0.3669 & 0.1538 & 0.5059 & 0.1750 \\ Disen-GNN & 0.1821 & 0.0759 & 0.2212 & 0.0819 & 0.6236 & 0.2701 & 0.7141 & 0.3120 & 0.3981 & 0.1769 & 0.5341 & 0.1879 \\ TAGNN & 0.1737 & 0.0699 & 0.1902 & 0.0782 & 0.6231 & 0.2698 & 0.7170 & 0.3101 & 0.3803 & 0.1611 & 0.5153 & 0.1790 \\ \hline DHCN & 0.1499 & 0.0563 & 0.1622 & 0.0590 & 0.6354 & 0.2635 & 0.7078 & 0.3029 & 0.3987 & 0.1753 & 0.5318 & 0.1844 \\ GCE-GNN & 0.1694 & 0.0803 & 0.2237 & 0.0840 & 0.6231 & 0.2707 & 0.7139 & 0.3121 & 0.4116 & 0.1815 & 0.5422 & 0.1904 \\ I3 GN & 0.1810 & 0.0761 & 0.2111 & 0.0833 & 0.6131 & 0.2701 & 0.7137 & 0.3129 & 0.3895 & 0.1721 & 0.5225 & 0.1829 \\ COTREC & 0.1571 & 0.0581 & 0.1703 & 0.0635 & 0.6242 & 0.2711 & 0.7113 & 0.3111 & 0.4179 & 0.1818 & 0.5431 & 0.1903 \\ \hline DEISI-GNN & **0.1932** & **0.0892** & **0.2349** & **0.0943** & **0.6732** & **0.2987** & **0.7563** & **0.3387** & **0.4392** & **0.1849** & **0.5573** & **0.1946** \\ \hline _Improv_ & 6.1\% & 11.1\% & 5.0\% & 12.3\% & 7.9\% & 10.2\% & 5.9\% & 8.2\% & 5.1\% & 1.7\% & 2.6\% & 2.2\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing the prediction performances of DEISI with the baselines. The best results in them are highlighted in bold, and the second-best results are underlined. We add the row _Improv_ to show the percentage performance improvement of our model compared with the best baseline. By comparing the experimental results, we can make the following four observations and analysis:  
(1) Compared with RNN-based and attention-based models, GNN-based models obviously perform better. It exhibits the great capability of GNN in learning more accurate embeddings and modeling session data. (2) Models that take into account inter-session information are expected to outperform those that only consider intra-session information, owing to the availability of more comprehensive data. However, our experimental findings reveal that this is not always the case. For instance, in the Nowplaying dataset, both DHCN and COTREC underperform compared with SR-GNN, Disen-GNN, and TAGNN. Likewise, in the Yoochoose1/64 dataset, Disen-GNN surpasses DHCN, and in the Dinetica dataset, Disen-GNN outperforms both DHCN and I3 GN. These observations suggest that incorporating inter-session information without prudently assessing its relevance and utility may lead to interference, rather than enhancement, in the performance of recommendation systems.