## 6 Results and Discussion
### Ablation Experiment  
To validate the improvements to GCLTE in rating prediction and reason generation, we will conduct ablation experiments on the model's components. The results for all datasets are presented in Table 7. In Table 7, \"GCLTE\" represents our model without any ablation of its modules. \"GCLTE\({}_{\text{W/CL}}\)\" represents GCLTE without the Contrastive Learning module, \"GCLTE\({}_{\text{W/E}}\)\" represents GCLTE without the information enhancement operation, and GCLTE\({}_{\text{GRU}}\) represents GCLTE using GRU as the decoder, where \"improve\" denotes the enhancements of GCLTE relative to its variants, with the enhancement values bolded. The calculation method for \"improve\" is as follows:  
\[\text{improve}=\frac{|\text{Metric}_{\text{GCLTE}}-\text{Metric}_{variants}|}{ \text{Metric}_{variants}} \tag{21}\]  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **BLEU1** & **BLEU4** & **ROUGE\_1/f** & **ROUGE\_1/r** & **ROUGE\_1/p** & **ROUGE\_2/f** & **ROUGE\_2/r** & **ROUGE\_2/p** \\ \hline Att2 seq & 12.8202 & 0.5419 & 18.4875 & 11.6949 & 44.9535 & 1.5134 & 0.9536 & 3.7028 \\ RETER & 7.8509 & 0.3690 & 11.0091 & 6.2566 & **47.6637** & 0.9135 & 0.5177 & 3.9592 \\ NRT & 11.4115 & 0.6270 & 16.0631 & 10.1750 & 38.9668 & 1.9961 & 1.2563 & **4.8961** \\ EMER & 13.3753 & 0.7190 & 17.6375 & 11.6058 & 37.6826 & 2.0430 & 1.3805 & 4.0564 \\
**GCLTE** & **21.1844** & **1.1768** & **23.2173** & **21.0422** & 26.1984 & **3.0755** & **2.9826** & 3.2088 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reason generation based on Amazon_Instant_Video dataset (test set).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **BLEU1** & **BLEU4** & **ROUGE\_1/f** & **ROUGE\_1/r** & **ROUGE\_1/p** & **ROUGE\_2/f** & **ROUGE\_2/r** & **ROUGE\_2/p** \\ \hline Att2 seq & 20.5806 & 3.5184 & 22.5084 & **20.4895** & 26.0100 & 5.6244 & **5.2796** & 6.2503 \\ RTER & 17.1456 & 2.1726 & 19.6311 & 14.8421 & 29.4247 & 4.8076 & 3.5978 & **7.3658** \\ NRT & 18.5425 & 3.3937 & 20.6744 & 16.8437 & 27.9985 & 5.6636 & 4.7378 & 7.4232 \\ EMER & 18.6114 & 2.1851 & 22.0573 & 17.3172 & **30.9540** & 4.8569 & 4.0701 & 6.1467 \\
**GCLTE** & **20.8766** & **3.4942** & **22.7577** & 18.6529 & 27.1614 & **6.0271** & 5.2776 & 7.1744 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reason generation based on REASONER dataset (test set).  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **BLEU1** & **BLEU4** & **ROUGE\_1/f** & **ROUGE\_1/r** & **ROUGE\_1/p** & **ROUGE\_2/f** & **ROUGE\_2/r** & **ROUGE\_2/p** \\ \hline Att2 seq & 17.5467 & 1.5001 & 18.9466 & 14.9544 & 34.0016 & 3.2864 & 2.5508 & 6.8905 \\ RETER & 7.8509 & 0.3690 & 11.0091 & 6.2566 & 47.6637 & 0.9135 & 0.5177 & 3.9592 \\ NRT & 13.5462 & 1.1856 & 16.1556 & 10.2688 & 38.6763 & 3.3596 & 2.1277 & 8.1914 \\ EMER & 8.9918 & 0.7997 & 9.5330 & 5.2827 & **66.2802** & 1.9228 & 1.0618 & **14.1053** \\
**GCLTE** & **21.4245** & **1.7071** & **22.2740** & **17.8240** & 30.0254 & **4.0968** & **3.3816** & 5.2516 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reason generation based on Amazon_Music dataset (test set).