## 4 Experiments
### Experiment Results  
We employ three widely used evaluation metrics, namely MSE, MAE, and ACC, to assess the performance of SCTD and other baseline models. The comparative experimental results are presented in Table 3. As evidenced by the results, SCTD surpasses the state-of-the-art baseline on the Yelp dataset, which unequivocally attests to the efficacy of the proposed model. To gain deeper insights, we further dissect the experimental results. Notably, matrix decomposition-based methods significantly outperform other approaches, underscoring the superiority of leveraging matrix decomposition techniques. Within the realm of matrix decomposition methods, static methods consistently underperform compared to their time-dynamic counterparts. This observation leads us to conclude that in real-world scenarios where user preferences exhibit dynamic biases, static approaches inevitably compromise recommendation performance. Among the time-dynamic methods, IMF-DANCER excels in capturing temporal dynamics, adeptly modeling real-world user attributes. However, in data-scarce environments, SCTD demonstrates superior performance over IMF-DANCER. This is achieved through the construction of the SCS, which effectively mitigates data imbalance and sparsity issues. Furthermore, SCTD integrates the TDR to enhance the representational capacity of the original dataset. It is worth noting that CISD is a model for mitigating sentiment bias, and although CISD introduces causality to address sentiment bias, it is less effective in the comparison of the above assessment metrics because the model does not use the IPS method. To rigorously demonstrate that the robustness of SCTD surpasses the aforementioned baseline models, we constructed an imbalanced dataset, Imbalanced-Yelp, designed to simulate real-world data conditions. Specifically, we adopted a node-based approach, systematically deleting 10% of the reviews at each step, and sequentially investigated the impact of deleting reviews ranging from 0% to 100% for each method under study. When 0% of the reviews are deleted, Imbalanced-Yelp is identical to the original Yelp dataset, representing a complete and balanced scenario. Conversely, when 100% of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MSE** & **MAE** & **ACC** \\ \hline Avg & 0.3155 & 0.4321 & 0.3623 \\ T-Avg & 0.328 & 0.4326 & 0.3614 \\ MF & 0.1811 & 0.3314 & 0.468 \\ IMF & 0.1338 & 0.2818 & 0.5396 \\ \hline MF-StaticIPS & 0.1879 & 0.3377 & 0.4598 \\ IMF-StaticIPS & 0.1086 & 0.2491 & 0.6065 \\ MF-DANCER & 0.1533 & 0.3032 & 0.5074 \\ IMF-DANCER & 0.1045 & 0.2444 & 0.6151 \\ CISD & 0.1687 & 0.2958 & 0.5633 \\ \hline SCTD & 0.1032 & 0.2527 & 0.6202 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with different methods on the Yelp dataset. reviews are deleted, Imbalanced-Yelp is reduced to a ratings-only dataset, simulating an extreme case of data sparsity. The experimental results, illustrated in Figure 4, reveal significant insights. On the Imbalanced-Yelp dataset, the performance of the comparison models TMF-StaticIPS, MF-DANCER, and TMF-DANCER decreases rapidly as the degree of missing user comments continues to increase, while the performance of SCTD as well as MF-StaticIPS remains relatively stable. The performance of TMF-DANCER remains almost the same as that of SCTD when the deleted comments are 0%, but as the degree of missing user comments continues to increase until it reaches 100%, the MSE value of TMF-DANCER improves to 0.1563, while that of SCTD is 0.1379. This highlights the challenges posed by real-world data imbalances and underscores the limitations of TMF-DANCER in such scenarios. In contrast, SCTD maintains robust performance across varying levels of data imbalance, demonstrating its superior adaptability. Additionally, it is noteworthy that MF-StaticIPS, a method based on static IPS, exhibits relatively stable performance throughout the experiments.