## 4 Experiments
### Results of Ablation Experiments(RQ2)  
#### 4.5.1 Contrast Loss  
Contrast learning facilitates model fitting and interpretability by learning the similarity between long and short features, combined with dynamic weight assignment. We conducted ablation experiments on contrast loss, we compared the performance of two models LUTUR, SLIREC with and without contrast loss, and replaced the contrast loss function of CLSR. The experimental effects of dynamic assignment of weights were also compared, and Table 4 shows all the comparison results in detail.  
As can be seen from Table 5, the contrast loss adaptation can effectively improve the performance of the LS-term model and is applicable to difficult recommendation scenarios. The assignment of dynamic weights can serve to effectively assign the weights of long and short features. We replaced the contrast loss in the CLSR model, which outperformed our model by 0.35% on the Amazon dataset and by 0.13% in the cold start case. This indicates that the decoupling framework in CLSR is applicable to most domains and can effectively improve model performance, and that self-supervised decoupling on how to do so effectively is a future direction for improvement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Category**} & \multicolumn{4}{c}{**Long-Term**} & \multicolumn{4}{c}{**Short-Term**} & \multicolumn{4}{c}{**LS-Term**} \\ \hline
**Dataset** & **Model** & **DIN** & **PACA** & **NARM** & **RCNN** & **CASER** & **GRUREC** & **DIEN** & **FMLP** & **LUTUR** & **SLIREC** & **CLSR** & **CDF-LS** \\ \hline \multirow{4}{*}{Amazon} & ACC & 0.7148 & 0.7057 & 0.7364 & 0.7698 & 0.7665 & 0.7747 & 0.7805 & 0.7881 & 0.7924 & 0.8002 & 0.8046 & 0.8014 \\  & AUC & 0.8095 & 0.8154 & 0.8340 & 0.8465 & 0.8415 & 0.8574 & 0.8636 & 0.8716 & 0.8786 & 0.8773 & 0.8857 & 0.8824 \\  & F1 & 0.7167 & 0.7096 & 0.7310 & 0.7668 & 0.7633 & 0.7789 & 0.7774 & 0.7830 & 0.7911 & 0.7973 & 0.8077 & 0.8096 \\ \hline \multirow{4}{*}{Taobao} & ACC & 0.6895 & 0.7033 & 0.7021 & 0.7174 & 0.7122 & 0.7189 & 0.7296 & 0.7374 & 0.7561 & 0.7543 & 0.7607 & 0.7724 \\  & AUC & 0.7624 & 0.7617 & 0.7273 & 0.8084 & 0.7096 & 0.8087 & 0.8390 & 0.8389 & 0.8319 & 0.8318 & 0.8388 & 0.8392 \\  & F1 & 0.6941 & 0.7097 & 0.7029 & 0.7218 & 0.7145 & 0.7187 & 0.7279 & 0.7448 & 0.7719 & 0.7693 & 0.7691 & 0.7710 \\ \hline \multirow{4}{*}{Douban} & ACC & 0.8549 & 0.8440 & 0.8699 & 0.8740 & 0.8710 & 0.8811 & 0.8951 & 0.8941 & 0.9066 & 0.9018 & 0.9132 & 0.9134 \\  & AUC & 0.8974 & 0.8838 & 0.9174 & 0.9281 & 0.9204 & 0.9168 & 0.9286 & 0.9378 & 0.9454 & 0.9463 & 0.9481 & 0.9567 \\  & F1 & 0.8577 & 0.8369 & 0.8787 & 0.8817 & 0.8763 & 0.8837 & 0.8938 & 0.8869 & 0.9010 & 0.9086 & 0.9047 & 0.9079 \\ \hline \multirow{4}{*}{Yelp} & ACC & 0.6566 & 0.6610 & 0.6834 & 0.7093 & 0.7307 & 0.7399 & 0.7524 & 0.7580 & 0.7630 & 0.7719 & 0.7818 & 0.7907 \\  & AUC & 0.7035 & 0.7271 & 0.7319 & 0.7504 & 0.7809 & 0.7807 & 0.8036 & 0.8073 & 0.8098 & 0.8122 & 0.8164 & 0.8204 \\ \cline{1-1}  & F1 & 0.6589 & 0.6683 & 0.6803 & 0.7017 & 0.7393 & 0.7271 & 0.7513 & 0.7515 & 0.7641 & 0.7783 & 0.7730 & 0.7943 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The red font indicates the best results, and the LS-term models all use the RCNN and FMLP user vector weights for the best results.  
Figure 2: LUTUR, SLIREC, CLSR and CDF-LS on four datasets with loss descent plots.