## 5 Conclusions  
In this paper, we present an experimental analysis of the differences in recommendation outcomes between long and short user interest models. To address this gap, we propose a novel interest fusion module that assimilates both long and short user interests. Our module is designed as a plug-and-play feature that employs a shared attention mechanism to fuse the long and short interest features. Additionally, we incorporate a comparison task to assess the similarity between the two interest representations and the fused interest representations. Finally, we evaluated the effectiveness of our proposed module for recommendation results on multiple datasets.  
Conceptualization, K.L. and W.W.; methodology, K.L. and W.W.; software, W.W.; validation, K.L. and W.W.; formal analysis, R.W.; investigation, X.C.; resources, L.Z.; data curation, X.Y.; writing--original draft preparation, W.W.; writing--review and editing, K.L. and X.L.; visualization, R.W., X.C. and X.L.; supervision, L.Z. and X.Y.; project administration, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Natural Science Foundation of China (No. 62202390), Science and Technology Fund of Sichuan Province (No. 2022 NSFSC0556), and the Opening Project of Lab of Security Insurance of Cyberspace, Sichuan Province.  
* Amazon: [[https://nijianmo.github.io/amazon/index.html](https://nijianmo.github.io/amazon/index.html)]([https://nijianmo.github.io/amazon/index.html](https://nijianmo.github.io/amazon/index.html)); Taobao: [[https://tianchi.aliyun.com/dataset/649](https://tianchi.aliyun.com/dataset/649)]([https://tianchi.aliyun.com/dataset/649](https://tianchi.aliyun.com/dataset/649)); Douban: [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BYEBk1 nkcX538 ucCddgFnw?pwd=1234]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) BYEBk1 nkcX538 ucCddgFnw?pwd=1234); Yelp: [[https://www.yelp.com/dataset](https://www.yelp.com/dataset)]([https://www.yelp.com/dataset](https://www.yelp.com/dataset)).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Sequences**} & \multicolumn{2}{c}{**Amazon**} & \multicolumn{2}{c}{**Yelp**} \\ \cline{2-9}  & **20** & **30** & **40** & **50** & **AUC** & **F1** & **AUC** & **F1** \\ \hline \multirow{6}{*}{DIN + CASER} & \(\bigvee\) & \(\bigvee\) & & & 0.8427 & 0.7696 & 0.7909 & 0.7415 \\  & \(\bigvee\) & & \(\bigvee\) & & 0.8715 & 0.7866 & 0.8123 & 0.7501 \\  & \(\bigvee\) & & & \(\bigvee\) & **0.8753** & **0.7917** & **0.8149** & **0.7553** \\  & & \(\bigvee\) & & & 0.8631 & 0.7898 & 0.8077 & 0.7430 \\  & & \(\bigvee\) & & \(\bigvee\) & 0.8649 & 0.7910 & 0.8083 & 0.7431 \\  & & & \(\bigvee\) & & 0.8548 & 0.7704 & 0.7905 & 0.7257 \\ \hline \multirow{6}{*}{FMLP + RCNN} & \(\bigvee\) & \(\bigvee\) & & 0.8701 & 0.7792 & 0.8075 & 0.7547 \\  & & \(\bigvee\) & & 0.8809 & 0.7903 & 0.8166 & 0.7724 \\ \cline{1-1}  & & & \(\bigvee\) & & **0.8847** & **0.7953** & **0.8189** & **0.7764** \\ \cline{1-1}  & & & & \(\bigvee\) & 0.8703 & 0.7765 & 0.8086 & 0.7561 \\ \cline{1-1}  & & & & \(\bigvee\) & 0.8761 & 0.7846 & 0.8137 & 0.7704 \\ \cline{1-1}  & & & & \(\bigvee\) & 0.8602 & 0.7739 & 0.8060 & 0.7433 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Underline represents the baseline model for the combination, bolded represents the best result. The longer sequences in each row are used to train the long-term interest model, and the shorter sequences are used to train the short-term interest model.