# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 6 Results and Analysis
### RQ3: Ablation Studies  
To answer the research question RQ3, we conduct ablation experiments in this section by comparing our model with two variants. The first variant is the original SASRec model optimized by the loss function \(L_{w}\). We aim to investigate the performance of a single sequence encoder optimized by both accuracy and calibration. We also reported the performances when the dimension of hidden states equals to 64 and 128 (namely \(SASRec_{\text{D128}}^{L_{w}}\) and \(SASRec_{\text{D128}}^{L_{w}}\)). In addition, we directly add the extractor nets to the \(SASRec_{\text{D128}}^{L_{w}}\) model, namely \(SASRec_{\text{D128}}^{L_{w}}EX\). The other one is the direct concatenation of sequence representations and item embedding matrices without extraction nets (namely DACSR-C). We compare our model with these variants by setting \(\lambda=0.5\). The performances are listed in Table 5. In general, our DACSR model obtains the best performance in terms of recommendation accuracy and calibration. The effectiveness of our designed loss function for calibration can be reflected by the performance of \(SASRec_{\text{D64}}^{L_{w}}\) and \(SASRec_{\text{D128}}^{L_{w}}\). By applying the loss function \(L_{w}\), the SASRec model is able to provide more calibrated recommendation lists than the original SASRec model only optimized by \(L_{Acc}\). For example, the \(C_{\text{KL}}@\)020 of SASRec on the Tmall dataset decreases from 2.1103 to 1.8845. Also, it achieves close performance compared to our DACSR model on the MI-1m dataset in terms of calibration. The calibration performances of the SASRec model optimized by the weighted loss function \(L_{w}\) verified the effectiveness of our proposed loss function. The performances between our model and variants also demonstrate the effectiveness of the decoupled-aggregated framework. For example, on the MI-1m dataset, the MRR@20 of our DACSR model is 0.1338, while it is 0.1269 for the \(SASRec_{\text{D128}}^{L_{w}}\) model, and the per  
Figure 4: Performance comparison when \(\tau\) changes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Metrics** & \(SASRec_{\text{D64}}^{L_{w}}\) & \(SASRec_{\text{D128}}^{L_{w}}\) & \(SASRec_{\text{D128}}^{L_{w}}EX\) & **DACSR-C** & **DACSR** \\ \hline \multirow{8}{*}{MI-1m} & Rec@10 & 0.2551 & 0.2730 & 0.2715 & 0.2710 & **0.2811** \\  & MRR@10 & 0.1088 & 0.1206 & 0.1213 & 0.1187 & **0.1267** \\  & C\({}_{KL}@\)10 & 1.0780 & 1.0765 & 1.0835 & 1.0909 & **1.0615** \\  & Rec@20 & 0.3599 & 0.3657 & 0.3748 & 0.3719 & **0.3844** \\  & MRR@20 & 0.1161 & 0.1269 & 0.1283 & 0.1257 & **0.1338** \\  & \(C_{KL}@\)020 & 0.7281 & **0.7253** & 0.7315 & 0.7433 & 0.7262 \\ \hline \multirow{8}{*}{Tmall} & Rec@10 & 0.1508 & 0.1482 & 0.1462 & 0.1464 & **0.1517** \\  & MRR@10 & 0.0854 & 0.0862 & **0.0864** & 0.0813 & 0.0857 \\ \cline{1-1}  & \(C_{KL}@\)010 & 2.1490 & 2.2499 & 2.3119 & **1.9816** & 2.0114 \\ \cline{1-1}  & Rec@20 & 0.1830 & 0.1787 & 0.1777 & 0.1804 & **0.1855** \\ \cline{1-1}  & MRR@20 & 0.0876 & 0.0884 & **0.0886** & 0.0837 & 0.0881 \\ \cline{1-1}  & C\({}_{KL}@\)020 & 1.7810 & 1.8845 & 1.9322 & **1.5960** & 1.6240 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparisons between our DACSR model and its variants. formances of calibration are close (0.7262 vs. 0.7253). On the Tmall dataset, our DACSR model can achieve competitive recommendation accuracy, and provide more calibrated recommendations (e.g., 1.6240 vs. 1.8845 in terms of \(C_{KL}\)@20). Compared to the \(SASRec_{D128}^{L_{w}}\) and \(SASRec_{D128}^{L_{w}}EX\) model which shares parameters for two objectives, the decoupled-aggregated framework can achieve better performance. We believe that such a framework can learn the information of two objectives and combine them to obtain better representations of sequences and items. While a single sequence encoder that improves the performance in one aspect may negatively affect performance in the other because their parameters are shared.