# Logit Averaging: Capturing Global Relation for Session-Based Recommendation
## 3 Results
### Ablation Study
#### 3.3.1 Effectiveness of Augmentation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Augmentation** & **LA** & **HR@10** & **MRR@10** & **Coverage@10** & **HR@20** & **MRR@20** & **Coverage@20** \\ \hline \multirow{3}{*}{RetailRocket} & X & O & \(-\)0.20 & 0.09 & \(-\)0.15 & \(-\)0.24 & 0.08 & \(-\)0.05 \\  & GraphMix & O & \(-\)0.44 & 0.88 & \(-\)1.73 & \(-\)0.81 & 0.85 & \(-\)1.50 \\  & FLAG & O & 1.18 & 0.54 & 2.04 & 1.20 & 0.56 & 2.00 \\  & Random deletion & O & 0.82 & \(-\)2.07 & 1.55 & 1.61 & \(-\)2.02 & 1.09 \\  & Random Insertion & O & \(-\)1.71 & \(-\)3.31 & \(-\)0.03 & \(-\)1.12 & \(-\)3.28 & \(-\)0.05 \\ \hline \multirow{3}{*}{Tmail} & X & O & \(-\)0.56 & \(-\)0.24 & 0.08 & \(-\)0.80 & \(-\)0.28 & 0.10 \\  & GraphMix & O & 1.70 & 0.95 & 1.46 & 2.15 & 0.97 & 2.72 \\  & FLAG & O & 0.46 & \(-\)0.32 & 0.18 & 1.42 & \(-\)0.27 & 0.25 \\  & Random deletion & O & \(-\)0.44 & \(-\)0.66 & 0.44 & \(-\)0.73 & \(-\)0.69 & 0.76 \\  & Random Insertion & O & \(-\)3.31 & \(-\)2.19 & 0.16 & \(-\)3.82 & \(-\)2.23 & 0.15 \\ \hline \multirow{3}{*}{Yocohose 1/64} & X & O & \(-\)0.01 & 0.17 & 0.48 & 0.03 & 0.17 & 0.63 \\  & GraphMix & O & \(-\)0.73 & \(-\)0.12 & 2.48 & \(-\)0.64 & \(-\)0.11 & 3.07 \\ \cline{1-1}  & FLAG & O & 0.28 & 0.11 & 0.62 & 0.26 & 0.11 & 0.53 \\ \cline{1-1}  & Random deletion & O & \(-\)0.15 & \(-\)0.82 & \(-\)1.79 & \(-\)0.02 & \(-\)1.06 & \(-\)1.92 \\ \cline{1-1}  & Random Insertion & O & \(-\)1.21 & \(-\)1.72 & \(-\)0.69 & \(-\)0.55 & \(-\)1.68 & \(-\)0.54 \\ \hline \multirow{3}{*}{Diginetica} & X & O & 0.62 & 0.31 & 1.41 & 0.69 & 0.32 & 1.50 \\ \cline{1-1}  & GraphMix & O & 0.48 & 0.32 & 1.40 & 0.33 & 0.31 & 0.93 \\ \cline{1-1}  & FLAG & O & 0.08 & 0.01 & 0.09 & \(-\)0.05 & 0.01 & 0.22 \\ \cline{1-1}  & Random deletion & O & \(-\)0.17 & \(-\)0.83 & 0.77 & 0.12 & \(-\)0.81 & 1.49 \\ \cline{1-1}  & Random Insertion & O & \(-\)0.25 & \(-\)0.37 & \(-\)0.27 & \(-\)0.44 & \(-\)0.37 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average performance gains (%) of four baseline methods with LA and different data augmentation methods on the original baseline methods. as in Equation (8). The loss value calculated from mixed logits, \(L_{m}\), is interpolated with respect to \(\lambda\) value as well. The final loss value is obtained in addition to the original loss \(L_{o}\) and the modified one, \(L_{m}\). FLAG [37] is introduced as a data augmentation technique, specifically for graph datasets to enhance the performance of the graph neural network through iteratively augmented node features with gradient-based adversarial perturbations during training. Lastly, we execute a random deletion and random selection method, which is a straightforward data augmentation method widely used in the natural language processing (NLP) domain. For each session within a batch, we randomly delete or insert the items to sessions. For random insertion, we use a set of unique items within a batch to randomly select the one item to insert. Table 3 shows the average percentage of the performance gain of different data augmentation methods in four baseline models, which are both attention-based (NARM, SRSAN) and GNN-based (NISER, TAGNN++). The raw result value of this experiment is shown in Table A1 in Appendix A. Even though the results differ depending on the datasets, GraphMix and FLAG show an impressive performance, improving Recall, MRR and item coverage. Especially for the Tmall dataset, GraphMix increases HR@20, MRR@20, and Coverage@20 by 2.15%, 0.97%, and 2.72%, respectively. However, applying random deletion and random insertion rather degrades the performance in almost all datasets. Thus, we could infer that GraphMix and FLAG are effective data augmentation techniques that could alleviate the data sparsity problem in session datasets, and further diversify the item recommendation lists.