## 4 Experiments
### Overall Performance Comparison (RQ1)  
Table 2 details the overall performance of all models on the four datasets, from which the following four results can be observed:  
* **The overall performance of the short-term interest model is better. ** From the overall results, the short-term interest model is better overall than the long-term interest model because it can capture the actual by-sequence information of user interaction well. The results from DIN, PACA, and RCNN show that the length interest features are more informative, and if more effective methods can be used to fit the long-term interest features, the improvement of the models' effectiveness is significant. * **The long-term interest model has the advantage of playing in two situations: a large variety of products and a long time span of user clicks. ** Although the short-term interest model is generally better, the short-term model does not necessarily outperform the long-term model for the two cases of large variety of items and long click time span. The RCNN and CASER models also use CNN networks, but CASER is slightly less effective than the RCNN model, but there is a large gap between them and FMLP, which indicates that the user's pre-click data helps the model to capture long-term interest, but the short-term interest weight is generally larger than the long-term interest weight. The cold start problem is a difficult problem for both models, and the best results for the Yelp dataset are lower than the remaining three datasets, and there is a large gap between the long-term and short-term models, which verifies that fitting to the length of the sequence data and effective extraction of the sequence data are the keys to improve the performance. * **Joint modeling of long and short interests is a generally effective approach. ** Joint modeling of models somewhat alleviates the poor performance of independent models in cold starts, large span of user clicks, and many types of user clicks, but it is not always effective. NARM, LUTUTR, and SLIREC are trained by entangling long and short interests with each other, which somewhat increases the redundancy of the models, and the performance of SLIREC in ACC and F1 which are inadequate. In contrast, CLRS decouples the calculation of long and short interests, and the adaptive weighting fuses the long and short interest weights, which alleviates the training volume of the model, which makes up for the lack of realizations of SLIREC on Taobao. * **Contrast learning and feature enhancement can effectively improve model performance. ** Our model differs slightly from the best comparison model CLRS on the data-rich dataset Taobao, but improves AUC by almost 0.01 on Douban, which has a much smaller variety of products, and for the cold-start case, our model leads CLRS in all metrics, and has into 1.3% improvement on the long time span Taobao dataset. The results from Table 3 also show that CDF-LS achieves a better balance in terms of computational cost. These all validate the effectiveness of contrast loss and feature enhancement. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Taobao**} & \multicolumn{3}{c}{**Yelp**} \\ \cline{2-7}  & **Params (G)** & **FLOPs (G)** & **Throughput (Samples/s)** & **Params (G)** & **FLOPs (G)** & **Throughput (Samples/s)** \\ \hline LUTUR & 29.02 & 1.28 & 250,246.81 & 52.79 & 0.64 & 248,976.63 \\ SLIREC & 28.98 & 2.37 & 64,913.81 & 52.75 & 2.54 & 70,198.13 \\ CLRS & 29.0 & 1.62 & 98,586.71 & 52.77 & 1.62 & 102,487.96 \\ CDF-LS & 31.57 & 1.20 & 79,161.06 & 54.84 & 1.21 & 84,611.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of computational cost of LS-term model on Taobao and Yelp datasets. Params represents the number of parameters of the model, FLOPs represents the floating point computation of the model, and the unit G = 1,000,000 for the above two metrics. Throughput represents the throughput of the model, and the unit Samples/s represents the maximum number of samples processed per second.