## 6 Results and Discussion
### Ablation Experiment
#### 6.3.2 Enhancement from Contrastive Loss  
To verify the performance enhancement from the embedding-based Contrastive Learning added to the graph convolutional neural network in the model's encoder, we removed the contrastive loss computation from the training process in GCLTE\({}_{\text{W/CL}}\) (without contrastive loss). From Table 7, it is evident that for both datasets, RMSE and MAE for GCLTE\({}_{\text{W/CL}}\) have improved values compared to GCLTE. This improvement signifies that  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Model** & **RSME** & **MAE** & **BLEU1** & **BLEU4** & **ROUGE\_1/f** & **ROUGE\_2/f** \\ \hline Reasoner & GCLTE & 1.1147 & 0.9189 & 20.8766 & 3.4942 & 22.7577 & 6.0271 \\ \cline{2-7}  & GCLTE\({}_{\text{W/CL}}\) & 1.2098 & 1.0023 & 18.1429 & 3.0023 & 20.0818 & 4.9880 \\  & **Improve** & **0.09** & **0.08** & **0.15** & **0.16** & **0.13** & **0.21** \\ \cline{2-7}  & GCLTE\({}_{\text{W/E}}\) & 1.1189 & 0.9201 & 16.6446 & 2.0450 & 19.2808 & 4.7288 \\  & **Improve** & **0.01** & **0.01** & **0.25** & **0.71** & **0.18** & **0.27** \\ \cline{2-7}  & GCLTE\({}_{\text{GRU}}\) & 1.1145 & 0.9193 & 16.1304 & 2.8761 & 18.6526 & 3.7841 \\  & **Improve** & **0.01** & **0.01** & **0.29** & **0.21** & **0.22** & **0.59** \\ \hline Music & GCLTE & 1.0358 & 0.8065 & 21.4245 & 1.7071 & 22.2740 & 4.0968 \\ \cline{2-7}  & GCLTE\({}_{\text{W/CL}}\) & 1.0967 & 0.8573 & 18.6119 & 1.2658 & 20.9199 & 3.1654 \\  & **Improve** & **0.06** & **0.6** & **0.15** & **0.35** & **0.06** & **0.29** \\ \cline{2-7}  & GCLTE\({}_{\text{W/E}}\) & 1.0454 & 0.8021 & 13.9831 & 0.7928 & 16.1360 & 1.8572 \\  & **Improve** & **0.01** & **0.01** & **0.53** & **1.15** & **0.38** & **1.20** \\ \cline{2-7}  & GCLTE\({}_{\text{GRU}}\) & 1.0397 & 0.8225 & 14.3898 & 1.1689 & 17.6789 & 3.3935 \\  & **Improve** & **0.01** & **0.02** & **0.49** & **0.46** & **0.26** & **0.21** \\ \hline Video & GCLTE & 1.0816 & 0.8476 & 21.1844 & 1.1768 & 23.2173 & 3.0755 \\ \cline{2-7}  & GCLTE\({}_{\text{W/CL}}\) & 1.1193 & 0.8844 & 17.0556 & 1.0914 & 20.6873 & 2.6436 \\  & **Improve** & **0.03** & **0.04** & **0.24** & **0.08** & **0.12** & **0.16** \\ \cline{2-7}  & GCLTE\({}_{\text{W/E}}\) & 1.0817 & 0.8473 & 14.1067 & 0.8425 & 16.9328 & 1.7363 \\  & **Improve** & **0.01** & **0.01** & **0.50** & **0.40** & **0.37** & **0.77** \\ \cline{2-7}  & GCLTE\({}_{\text{GRU}}\) & 1.0901 & 0.8440 & 14.3178 & 0.6712 & 18.1082 & 1.8650 \\  & **Improve** & **0.01** & **0.01** & **0.48** & **0.75** & **0.28** & **0.65** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study based on Amazon dataset (test set).  
adding noise to the embedding nodes while calculating contrast loss during our training process beneficially enhances the model's rating prediction capability. Additionally, text evaluation metrics show a slight improvement in GCLTE compared to GCLTEW/CL. This is attributed to the information enhancement operation performed on the text embeddings using the hidden state of the GCN layer during the input phase of the transformer decoder. This indicates that the hidden state, enhanced by the contrastive loss, continues to positively influence the input of the transformer.