# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 6 Results and Analysis
### RQ1: Overall Performance  
In this section, we answer the research question RQ1 about whether our model can provide calibrated and accurate recommendations. The performances of baselines and our model are listed in Table 2, where the best performance is marked in bold. **Recommendation Accuracy** We first analyze the performance from the perspective of accurate recommendation (i.e., Rec@K and MRR@K). In general, on both datasets, our model achieves the best prediction accuracy in terms of Recall and MRR. By considering calibration, users' preference distributions are incorporated. In addition, our model decoupled the two objectives by two sequence encoders and aggregated their outputs. Therefore, the preference distribution contributed to the prediction of the next item, leading to the improvement of accuracy. For example, on the MI-1m dataset, the Recall and MRR of our model are higher than the original SASRec model (e.g., 0.1338 vs. 0.1271 in terms of MRR@20). In contrast, the post-processing-based models fragmented the relationship between accuracy and calibration and therefore resulted in a reduction in accuracy. For example, on the MI-1m dataset, the MRR@20 of our model is 0.1338, while it is 0.1168 and 0.0784 for CaliRec and CaliRec-GC model, with the improvement of 18.67% and 74.66%, respectively. On the Tmall dataset, the CaliRec model also decreases the prediction accuracy. **Calibrated Recommendation** Our model can provide more calibrated recommendation lists compared to the original sequential recommendation model. On both datasets, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Metrics** & **SASRec** & **CaliRec** & **CaliRec-GC** & **DACSR** \\ \hline \multirow{8}{*}{ML-1m} & Rec@10 & 0.2627 & 0.2636 & 0.2225 & **0.2811** \\ \cline{2-6}  & MRR@10 & 0.1203 & 0.1101 & 0.0712 & **0.1267** \\ \cline{2-6}  & \(C_{KL}\)@10 & 1.2385 & 0.9722 & **0.4553** & 1.0615 \\ \cline{2-6}  & Rec@20 & 0.3613 & 0.3616 & 0.3258 & **0.3844** \\ \cline{2-6}  & MRR@20 & 0.1271 & 0.1168 & 0.0784 & **0.1338** \\ \cline{2-6}  & \(C_{KL}\)@20 & 0.8548 & 0.7322 & **0.3847** & 0.7262 \\ \hline \multirow{8}{*}{Tmall} & Rec@10 & 0.1451 & 0.1464 & 0.1454 & **0.1517** \\ \cline{2-6}  & MRR@10 & **0.0862** & 0.0846 & 0.0861 & 0.0857 \\ \cline{1-1} \cline{2-6}  & \(C_{KL}\)@10 & 2.5004 & 2.0710 & 2.4139 & **2.0114** \\ \cline{1-1} \cline{2-6}  & Rec@20 & 0.1749 & 0.1753 & 0.1751 & **0.1855** \\ \cline{1-1} \cline{2-6}  & MRR@20 & **0.0883** & 0.0866 & 0.0882 & 0.0881 \\ \cline{1-1} \cline{2-6}  & \(C_{KL}\)@20 & 2.1103 & 1.7943 & 2.0459 & **1.6240** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performances of our models and baselines (best performances are marked in bold). \(C_{KL}\)@10 and \(C_{KL}\)@20 of our model are lower than the original SASRec model. For example, the \(C_{KL}\)@20 of our model is 0.7262, which is 15.04% better than the 0.8548 of the SASRec model. On the Tmall dataset, our model also achieves a 23.04% improvement in terms of \(C_{KL}\)@20. Compared to the post-processing-based CaliRec model, our model still achieves competitive performances in terms of calibration. For example, on the ML-1m dataset, the performances of \(C_{KL}\)@20 of our model and the CaliRec model are 0.7262 and 0.7322. On the Tmall dataset, our model achieves an improvement of 9.49% in terms of \(C_{KL}\)@20. The comparisons show the ability of our model to achieve better accuracy while obtaining competitive performances of calibration compared to the post-processing-based models. As for the possible reasons, on the one hand, the proposed loss function calibrated the preference distribution of items with the highest scores to the historical preference distribution. On the other hand, the decoupled-aggregated framework ensures accuracy when improving the calibration. We also observe that the CaliRec-GC model performs differently on the two datasets.