## 7 Conclusions and Future Work  
In this paper, we introduce a multi-task learning model designed for interpretable recommendations. This model, based on an Encoder-Decoder structure, enhances convolutional neural networks with Contrastive Learning techniques and includes a transformer module. We integrate rating prediction and reason generation through a simple and efficient information enhancement operation. Our comprehensive experiments on benchmark datasets demonstrate that integrating two tasks leads to significant performance improvements. Additionally, experimental results demonstrate that GCLTE surpasses current state-of-the-art models in both rating prediction and reason generation tasks. However, GCLTE has not yet been tested on very large datasets. In future work, we plan to refine GCLTE's testing outcomes on these datasets and make targeted improvements. Given the transformer module in GCLTE, we anticipate that it will perform exceptionally well on very large datasets. Moreover, we aim to enhance the model's explainability, particularly in terms of reason generation. **Author Contributions:** Methodology, X.Z. ; Software, X.Z. and Y.W. ; Validation, Y.W. ; Formal analysis, W.Z. ; Investigation, X.Z. and X.X. ; Data curation, X.Z. ; Writing--original draft, X.Z. ; Writing--review & editing, X.X. ; Visualization, W.Z. ; Project administration, X.X. ; Funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This study is supported by Social Science Planning Project of Shandong Province (Grant No. 24 BJYJ02). **Data Availability Statement:** The original data presented in the study are openly available in [39; 40]. **Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Total Time (s)** & **Epochs** & **Time/Epoch (s)** & **GPU Memory Usage (MiB)** \\ \hline NRT & 130.1 & 38 & 2.6 & 2429 \\ PETER & 222.3 & 29 & 5.1 & 3243 \\ GCLTE & 116.5 & 13 & 4.5 & 3807 \\ \hline \hline \end{tabular}
\end{table} 
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