# Logit Averaging: Capturing Global Relation for Session-Based Recommendation
## 3 Results
### Experiment Result  
Table 2 shows the performance of the baseline models with and without Logit Averaging (LA). The rows are the baselines and their combination with LA, and columns are the evaluation metrics. We evaluate the performance in both top-10 and top-20 recommendation lists. For each dataset, we calculate the average percentage of the performance gain in performance in all six baseline models. At the bottom of Table 2, there is the Total Gain (%) that calculates the average percent of the gain in all four datasets. For all six metrics, HR@10, MRR@10, Coverage@10, HR@20, MRR@20, and Coverage@20, applying LA to the baselines improves the average performance by 0.48%, 1.86%, 2.78%, 0.26%, 1.78%, and 2.90%, respectively. This result proves the effectiveness of our proposed method in improving prediction accuracy and item diversity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Model** & **LA** & **HR@10** & **MRR@10** & **Coverage@10** & **HR@20** & **MRR@20** & **Coverage@20** \\ \hline \multirow{8}{*}{Dignetica} & NARM & 35.86 & 18.60 & 68.78 & 44.83 & 19.22 & 82.60 \\  & EOPA & 34.25 & 14.52 & 62.09 & 47.09 & 15.37 & 75.91 \\  & NISER & 34.12 & 17.98 & 65.02 & 41.89 & 18.52 & 77.34 \\  & SR-GNN & 32.84 & 16.83 & 60.95 & 41.46 & 17.43 & 71.44 \\  & SR-SAN & 32.54 & 16.61 & 67.60 & 41.50 & 17.22 & 78.56 \\  & TAGNN++ & 33.90 & 17.34 & 62.81 & 42.64 & 17.95 & 74.00 \\ \hline \multirow{4}{*}{Dignetica} & NARM & 35.76 & 18.51 & 69.05 & 44.49 & 19.12 & 83.07 \\  & EOPA & 34.54 & 14.60 & 62.33 & 47.03 & 15.47 & 75.81 \\  & NISER & O & 33.79 & 18.02 & 65.28 & 41.68 & 18.57 & 78.25 \\  & SR-SAN & O & 33.85 & 17.72 & 64.47 & 42.03 & 18.29 & 76.34 \\  & SR-SAN & 32.93 & 16.93 & 66.68 & 42.02 & 17.56 & 77.34 \\  & TAGNN++ & 36.42 & 18.31 & 68.85 & 45.41 & 18.93 & 79.83 \\ \cline{2-6}  & **Gain (\%)** & 1.88 & 2.18 & 2.53 & 1.29 & 2.13 & 2.47 \\ \hline \multicolumn{2}{c}{**Total Gain (\%)**} & 0.48 & 1.86 & 2.78 & 0.26 & 1.78 & 2.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._For RetailRocket dataset, HR@10, MRR@10, and Coverage@10 have increased by 0.86%, 3.23%, and 1.97%, respectively. HR@20, MRR@20, and Coverage@20 have increased by 0.54%, 3.14%, and 1.84%, respectively. Not only does prediction accuracy improve but also diversify the recommendation list. In the case of the Tmall dataset, MRR@10 and Coverage@10 have increased by 0.86% and 2.02% while HR@10 decreases by \(-0.98\%\). Even though there has been a slight decrease in Recall, applying LA helps to make an accurate reciprocal ranking prediction and increases the item diversity. For the Yoochoose 1/64 dataset, the performance of all three metrics, HR@10, MRR@10, and Coverage@10, improves by 0.15%, 1.18%, and 4.58%, respectively. It particularly increases item coverage more than other metrics. As [PERSON] et al. [35] pointed out, usually, recommendation accuracy is tied with diversity in a delicate trade-off. Thus, a great increase in item coverage along with recommendation accuracy when LA is applied is a remarkable achievement. Additionally, for the Diginetica dataset, HR@10, MRR@10, and Coverage@10 increase by 0.48%, 2.18%, and 2.53% respectively when LA is applied to the original baselines. This trend can also be observed when \(K=20\). The performance of HR@20, MRR@20, and Coverage@20 improves by 1.29%, 2.13%, and 2.47%. The six baseline models have shown a different tendency to improve recommendation results depending on whether the model is based on an attention mechanism or GNNs. Firstly, the attention-based models, NARM and SR-SAN, show a large improvement in Hit Ratio and MRR after training with LA methods. SR-SAN gains 13.99% on MRR@10, but drops 3.2% on Coverage@10 on average. The GNN-based models, EOPA, NISER, SR-GNN, and TAGNN++, show the opposite tendency of attention-based models. The models applying LA show the steepest rise in coverage. SR-GNN and TAGNN++ gain 49.48% and 22.15% on Coverage@10, respectively.