# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 6 Results and Analysis
### RQ2: Parameter Influence
#### 6.2.1 Trade-Off Factor \(\lambda\)  
We first analyze the influence of hyperparameter \(\lambda\) by changing it from 0.1 to 1.0. Since the CaliRec model also required a parameter to control the importance of calibration and accuracy which was similar to our model, we displayed the changes in the performance of our DACSR model and compared it with the CaliRec model simultaneously. The performances on the two datasets are shown in Figure 3, where red lines and blue lines represent our DACSR model and the CaliRec model, respectively.  
In general, a greater value of \(\lambda\) leads to a lower recommendation accuracy and higher calibration. When \(\lambda\) has a high value (e.g., \(\lambda=0.7\)), the performance of Rec@20 of our model decreases greatly, while the CaliRec model does not decrease as large as our model. A possible reason is the difference in the process of generating recommendation lists. The CaliRec re-ranks the candidate list generated by the SASRec model whose size is 100 and finally selects top-K (K = 10 or 20) items as the final recommendation list. However, our model computes scores of all items, and directly selects top-K items. For some sequences, the users' next items achieve relatively lower ranks. When considering more for calibration, these items may be replaced by items whose attributes are consistent with the historical distributions but not sequential correlated to the sequences. While for post-processing methods, the range of items for re-ranking is narrowed. Therefore, it can preserve the performance of Rec@20. In contrast, our model outperforms CaliRec when considering ranking performances in terms of MRR. As shown in Figure 3c,d, the MRR@20 of our model is always higher than the CaliRec model. This is because although target items may be replaced, these items do not contribute to the ranking performance greatly. In contrast, our model improves the ranking of the target items for most of the sequences, resulting in the improvement in overall ranking performance.  
In terms of calibration, our model achieves better performances compared to the CaliRec model under the same \(\lambda\) in general. On the Tmall dataset, the performances of calibration of our DACSR model are better than the CaliRec model during the major changing process of \(\lambda\). On the MI-1m dataset, the performances are close. Our model performs better than the CaliRec model when \(\lambda\) is greater than 0.5. When \(\lambda\) is close to 1, our DACSR model cannot perform as well as the CaliRec model. A possible reason is that the CaliRec model utilized a greedy-based ranking strategy so that it can select the most calibrated recommendations from top-100 items when \(\lambda\) is close to 1. In contrast, the deep learning-based optimization strategy always has a gap in fitting the target distribution and therefore does not perform as well as the CaliRec model at very high values of \(\lambda\). However, a large \(\lambda\) leads to lower recommendation accuracy. Though it achieves better calibration, it is not suggested to use high value of \(\lambda\) because accurately predicting the user's next behavior is still an important concern.