# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 7 Discussion  
In this paper, we proposed the DACSR model to provide accurate and calibrated recommendation lists for end-to-end sequential recommendation. We conducted experiments on benchmark datasets to demonstrate the effectiveness of our model. In general, our model achieved higher accuracy in predicting the next item and provided more calibrated recommendations compared to the post-processing-based model. This is because our \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **ILD@10** & \(\mathbf{C_{KL}@10}\) & **ILD@20** & \(\mathbf{C_{KL}@20}\) \\ \hline \(p(s)\) & 0.6714 & **2.0114** & 0.7045 & **1.6240** \\ \(p_{m}(s)\) & **0.7201** & 2.0539 & **0.7516** & 1.6288 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performances of distribution \(p_{m}(s)\) on the Tmall dataset. Figure 5: Illustration of amplified main interest. model considered the relationship between calibration and accuracy, which was isolated in post-processing-based models. Meanwhile, the end-to-end framework required much less time to provide recommendations than the post-process-based models. We displayed the trend in the model's performance as the two main hyperparameters change. As the parameter \(\lambda\) varies which stands for the importance of calibration in the objective function, our model achieved better performance in terms of accuracy and calibration. We also analyzed the influence of the parameter \(\tau\) which can change the predicted score distribution so that the model focused on items in different score segments. In the ablation study, we first demonstrated the effectiveness of the proposed loss function for calibration. By applying the calibration loss function, the sequential recommendation models became aware of the preference distribution of recommended items, and aligned it to the historical preference distribution. Therefore, the process of training and prediction was conducted in an end-to-end paradigm. Furthermore, with the decoupled-aggregated framework, the positive information for the calibrated and accurate recommendation was extracted from two individual sequence encoders to improve the performance. The performance comparisons between our model and its variants verified the necessity of the decoupled-aggregated framework. We finally investigated the effectiveness of our proposed distribution modification approaches. Because calibration is connected to diversity to a certain degree, we analyzed the relation between diversity and calibration. We also investigate the effect of imbalanced distribution of homogeneous interests. We found that it differed on the two datasets because of the item attribute coverage and the length of the sequence. For sequences with homogeneous preferences, considering calibration reduced the diversity of recommendations and amplified the main interests of the user. We adopted two distribution modification methods to improve the diversity and mitigate the effect of imbalanced distribution. The performances on two datasets verified the effectiveness of these approaches. However, there are some limitations in our work:  
* Calibration is not always equivalent to diversity, as analyzed in Section 6.4. For users with homogeneous interests, calibration decreases the performance of diversity. Therefore, user studies need to be conducted to analyze the acceptance of these users towards diversified and calibrated recommendations. This can provide data and theory support for recommendations. * Our model followed the conventional training framework which takes a sequence as input, predicts scores of all items and is optimized by a certain loss function. However, other advanced technologies and theories can be incorporated, such as contrastive learning [38; 39], few-shot learning [11; 40] and game theory [41; 42]
* We focused on providing calibrated recommendations for sequential recommendation models by designing loss functions and the decoupled-aggregated framework.