# Optimizing Session-Aware Recommenders: A Deep Dive into GRU-Based Latent Interaction Integration
## 4 Experimental Results
### Performance Comparisons
#### 4.4.3 Long-Term and Short-Term Fixed Ratio Performance  
In this section, we assess the recommendation performance using a fixed ratio (Fix) for long/short-term preferences. The experiment tested various parameter settings, with combinations of [long-term/short-term] preferences set at [0.8/0.2], [0.5/0.5], and [0.2/0.8]. Additionally, extreme setting combinations of [1/0] and [0/1] were evaluated, where the former exclusively considers long-term preferences and completely disregards short-term ones, while the latter does the opposite.  
The experimental results are summarized below. From the Amazon dataset, it is evident that both post-combine and pre-combine strategies achieve optimal performance with a fixed ratio of [0.5/0.5]. This finding suggests that the representation model requires a balanced consideration of both short-term and long-term preferences for best results. For the MovieLens 1M dataset, the post-combine outcome mirrors that of Amazon, with the optimal configuration for pre-combine being [0.8/0.2]. Notably, focusing solely on short-term preference [0/1] deteriorates model performance. Incorporating long-term information significantly enhances results, yet exclusively considering long-term preference [1/0] is not the most effective strategy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Window** & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** & **Recall** & **MRR** & **NDCG** \\
**Size (\%)** & **@5** & **@5** & **@5** & **@10** & **@10** & **@10** & **@20** & **@20** & **@20** \\ \hline
20 & 0.2356 & 0.2118 & 0.2009 & 0.2448 & 0.2131 & 0.2200 & 0.2522 & 0.2136 & 0.2268 \\
25 & 0.2364 & 0.2119 & 0.2015 & 0.2451 & 0.2131 & 0.2198 & 0.2537 & 0.2137 & 0.2269 \\
30 & 0.2343 & 0.2118 & 0.2009 & 0.2433 & 0.2130 & 0.2190 & 0.2512 & 0.2136 & 0.2263 \\
35 & 0.2340 & 0.2098 & 0.2002 & 0.2434 & 0.2111 & 0.2190 & 0.2523 & 0.2117 & 0.2266 \\
40 & 0.2357 & 0.2118 & 0.2008 & **0.2455** & 0.2131 & 0.2197 & **0.2540** & 0.2137 & **0.2275** \\
45 & 0.2356 & 0.2111 & 0.2013 & 0.2454 & 0.2124 & 0.2200 & 0.2534 & 0.2130 & 0.2274 \\
50 & 0.2357 & 0.2120 & 0.2014 & 0.2447 & 0.2133 & 0.2198 & 0.2526 & 0.2138 & 0.2262 \\
55 & **0.2366** & 0.2117 & 0.2009 & **0.2455** & 0.2128 & 0.2198 & 0.2532 & 0.2134 & 0.2272 \\
60 & 0.2359 & 0.2132 & 0.2014 & 0.2445 & 0.2143 & 0.2205 & 0.2524 & 0.2149 & 0.2274 \\
**65** & 0.2365 & **0.2134** & **0.2017** & 0.2451 & **0.2145** & **0.2206** & 0.2517 & **0.2150** & 0.2271 \\
70 & 0.2357 & 0.2122 & 0.2013 & 0.2446 & 0.2134 & 0.2201 & 0.2521 & 0.2139 & 0.2268 \\
75 & 0.2344 & 0.2100 & 0.2008 & 0.2429 & 0.2111 & 0.2191 & 0.2527 & 0.2118 & 0.2274 \\
80 & 0.2344 & 0.2115 & 0.2011 & 0.2432 & 0.2127 & 0.2192 & 0.2519 & 0.2133 & 0.2262 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance w.r.t. window size.  
Furthermore, the Steam dataset results indicate a bias towards short-term preference, implying that information features significantly impact dataset performance. Consequently, the best setting for post-combine is [0.2/0.8] and for pre-combine, is [0/1].