# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 4 Experimental Results
### Performance Comparisons
#### 4.4.4 Overall Comparisons  
The overall experimental results are presented in Tables 5-7, respectively, for datasets Amazon, MoveiLens 1M, and Steam. Most-Popular and Item-kNN are not included when their performance is far worse than the others. Our proposed method demonstrates the best performance in most metrics across the three datasets. Specifically, LCII yields the best results for Amazon, LCII-Pre\({}_{\text{Fix}}\) for MovieLens 1M, and LCII-Post\({}_{\text{Fix}}\) for Steam. In the MovieLens 1M dataset, LCII-Pre\({}_{\text{Fix}}\) outperforms II-RNN and BERT4 Rec(+ST+TSA) in Recall@20 by 1.85% and 2.54%, respectively, translating to relative increases of 7% and 9.9%. In the Steam dataset, LCII-Post\({}_{\text{LP}}\) shows a 18.66% and 5.5% higher performance in Recall@20 compared to II-RNN and BERT4 Rec(+ST+TSA), corresponding to relative improvements of 39.88% and 9.2%. For Amazon's Recall@20, LCII surpasses II-RNN and CAII by 2.59% and 1.89%, respectively, indicating performance boosts of 11.3% and 8%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** \\  & **@5** & **@5** & **@10** & **@10** & **@10** & **@20** & **@20** & **@20** \\ \hline II-RNN & 0.4196 & 0.3955 & 0.3945 & 0.4401 & 0.3982 & 0.4158 & 0.4679 & 0.4001 & 0.4277 \\ SASRec & 0.4945 & - & 0.4160 & 0.5218 & - & 0.4675 & 0.5551 & - & 0.4760 \\ BERT4 Rec & 0.4938 & - & 0.4622 & 0.5267 & - & 0.4729 & 0.5634 & - & 0.4820 \\ BERT4 Rec\({}_{+\text{ST+TSA}}\) & 0.5423 & - & 0.5175 & 0.5692 & - & 0.5262 & 0.5995 & - & 0.5338 \\ LCII & 0.5904 & 0.5353 & 0.5430 & 0.6192 & 0.5391 & 0.5778 & 0.6514 & 0.5414 & 0.5925 \\ LCII-Pre\({}_{LP}\) & 0.5860 & 0.5329 & 0.5409 & 0.6151 & 0.5368 & 0.5745 & 0.6450 & 0.5388 & 0.5885 \\ LCII-Pre\({}_{Fix}\) & 0.5923 & 0.5360 & 0.5453 & 0.6200 & 0.5397 & 0.5805 & 0.6527 & 0.5420 & 0.5965 \\ LCII-Post\({}_{LP}\) & 0.5936 & 0.5364 & 0.5449 & **0.6240** & 0.5404 & 0.5807 & **0.6545** & 0.5426 & 0.5945 \\
**LCII-Post\({}_{Fix}\)** & **0.5961** & **0.5410** & **0.5506** & 0.6232 & **0.5446** & **0.5837** & 0.6526 & **0.5466** & **0.5978** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overall performance w.r.t. MoveiLens 1M dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** \\  & **@5** & **@5** & **@10** & **@10** & **@10** & **@20** & **@20** & **@20** \\ \hline Most-Popular & 0.0041 & 0.0022 & 0.0026 & 0.0075 & 0.0025 & 0.0039 & 0.0144 & 0.0030 & 0.0065 \\ Item-kNN & 0.2172 & 0.1796 & 0.1820 & 0.2239 & 0.1804 & 0.2044 & 0.2260 & 0.1806 & 0.2143 \\ II-RNN & 0.2238 & 0.2139 & 0.1926 & 0.2264 & 0.2143 & 0.2080 & 0.2294 & 0.2145 & 0.2117 \\ CAII-P & 0.2295 & 0.2115 & 0.1929 & 0.2342 & 0.2121 & 0.2081 & 0.2364 & 0.2123 & 0.2120 \\ SASRec & 0.0908 & - & 0.0821 & 0.1007 & - & 0.0853 & 0.1102 & - & 0.0877 \\ BERT4 Rec & 0.1093 & - & 0.0900 & 0.1205 & - & 0.0936 & 0.1327 & - & 0.0967 \\ BERT4 Rec\({}_{+\text{ST+TSA}}\) & 0.1231 & - & 0.1060 & 0.1343 & - & 0.1096 & 0.1409 & - & 0.1113 \\ MCLRec & 0.1933 & - & 0.1601 & 0.1956 & - & 0.1597 & 0.1873 & - & 0.1534 \\ LCII & **0.2407** & **0.2157** & **0.2060** & **0.2487** & **0.2168** & **0.2244** & **0.2553** & **0.2173** & **0.2300** \\ LCII-Pre\({}_{