## 4 Experiments
### Robustness Test Experimental Results (RQ3)  
To demonstrate the versatility of our designed module for various models and scenarios, we conducted experiments on two challenging recommendation datasets using different models with varying sequence lengths for training the long and short interest models. The fusion module was utilized for pairing the models. Table 7 shows that the combination of the longest sequence model and the shortest sequence model outperforms  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Fusion**} & \multicolumn{2}{c}{**Amaxon**} & \multicolumn{2}{c}{**Yelp**} \\ \cline{3-6}  & & **AUC** & **F1** & **AUC** & **F1** \\ \hline \multirow{2}{*}{DIN + CASER} & ✗ & 0.8640 + 0.0225 & **0.7811** + 0.0178 & **0.7988** + 0.0179 & **0.7439** + 0.0046 \\  & ✓ & **0.8715** + 0.0300 & **0.7866** + 0.0233 & **0.8123** + 0.0314 & **0.7501** + 0.0118 \\ \multirow{2}{*}{NARM + DIEN} & ✗ & 0.8695 + 0.0059 & **0.7854** + 0.0008 & **0.8101** + 0.0065 & 0.7613 + 0.0100 \\  & ✓ & **0.8763** + 0.0127 & **0.7878** + 0.0104 & **0.8160** + 0.0124 & **0.7729** + 0.0216 \\ \multirow{2}{*}{PACA + GRU4 REC} & ✗ & 0.8641 + 0.0067 & **0.7811** + 0.0022 & **0.8010** + 0.0023 & **0.7684** + 0.0413 \\  & ✓ & **0.8720** + 0.0146 & **0.7869** + 0.0008 & **0.8103** + 0.0296 & **0.7700** + 0.0429 \\ \multirow{2}{*}{RCNN + FMLP} & ✗ & 0.8740 + 0.0024 & **0.7846** + 0.0016 & **0.8074** + 0.0001 & 0.7517 + 0.0002 \\  & ✓ & **0.8809** + 0.0093 & **0.7903** + 0.0073 & **0.8166** + 0.0093 & **0.7624** + 0.0109 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Four combinatorial models comparing the simple splicing method with our fusion method. The gray numbers represent the relative increase compared to the results of the original model. The maximum increase reached 0.0314.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Contrast**} & \multirow{2}{*}{**Weights**} & \multicolumn{2}{c}{**Amaxon**} & \multicolumn{2}{c}{**Yelp**} \\ \cline{3-6}  & & & **AUC** & **F1** & **AUC** & **F1** \\ \hline \multirow{2}{*}{LUTUR} & ✓ & & 0.8804 + 0.0018 & 0.8013 + 0.0102 & 0.8149 + 0.0051 & **0.7736** + 0.0095 \\  & ✓ & ✓ & 0.8820 + 0.0034 & 0.8049 + 0.0136 & 0.8157 + 0.0059 & **0.7746** + 0.0105 \\ \multirow{2}{*}{SLIREC} & ✓ & & 0.8853 + 0.0080 & 0.8051 + 0.0078 & 0.8166 + 0.0044 & **0.7804** + 0.0021 \\  & ✓ & ✓ & 0.8861 + 0.0088 & 0.8064 + 0.0091 & 0.8171 + 0.0049 & **0.7847** + 0.0063 \\ \multirow{2}{*}{CLSR} & ✓ & ✓ & 0.8859 + 0.0002 & 0.8101 + 0.0024 & **0.8217** + 0.0053 & **0.7881** + 0.0151 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contrast stands for using the comparison loss function, Weights stands for using dynamic weights to update the length of interest, and CLSR itself has dynamic weights, so only the new loss function is compared. The gray numbers indicate the increase relative to the original model results.  
the baseline model. However, when using similar models, the results were poorer. This is mainly due to the lack of compensating measures in similar models, which prevents the optimization of contrast loss based on differences between the two models. This can even result in interference, as evidenced by smaller results for 20 and 30 sequence lengths compared to 40 and 50 sequence lengths. Our tabular results demonstrate that our module significantly improves model performance in cases where there are large differences between long and short interest models. However, it is less effective for similar models, highlighting the limitations of contrast learning.