# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 8 Conclusions and Future Work  
In this paper, we were committed to exploring the provision of accurate and calibrated recommendations based on user behavioural sequences. We proposed a DACSR model to provide accurate and calibrated results under the end-to-end sequential recommendation framework. Specifically, we designed a loss function that estimates the preference distribution of the recommendation list by predicted scores of all items, and measures the consistency with the preference distribution of the user's historical behaviors. In addition, we proposed distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. To better handle the goals of accuracy and calibration,we proposed a decoupled-aggregated framework which includes two individual sequence encoders that were assigned with the accuracy and calibration objectives, respectively. Then we utilized an aggregation module to extract information from two sequence encoders to make both accurate and calibrated recommendations. According to experiments on benchmark datasets, our model can achieve better accuracy and calibration than the original sequence encoder and the post-processing methods. The ablation studies proved the effectiveness of the general architecture and the extractor net of our model. Finally, we investigated the connection between calibration and diversity, and prove the effectiveness of our proposed distribution modification approaches.  
For the future work, as mentioned in the discussion section, we first want to conduct user studies about the acceptance of diversity and calibration, so that diversified and calibrated recommendations can be supported. Furthermore, besides the conventional sequential recommendation training framework, incorporating advanced technologies and theories such as contrastive learning and game theory to debiasing recommendation is also one of our interests. In addition, we are also interested in exploring the reasons that cause the miscalibration, and applying reasons to improve the calibration performance. Finally, besides the calibrated recommendation, we hope to deal with different types of bias in recommendation algorithms.  
Conceptualization, J.C., W.W. and L.S.; methodology, J.C. and W.W.; software, J.C.; validation, J.C.; formal analysis, J.C. and W.W.; investigation, J.C. and W.W.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, J.C., W.W., L.S., Y.J., W.H., X.C., W.Z. and L.H.; resources, [PERSON]; visualization, J.C.; supervision, W.W. and L.H.; project administration, L.H.; funding acquisition, W.W. and X.C. All authors have read and agreed to the published version of the manuscript.  
This work is funded by National Natural Science Foundation of China (under project No. 61907016), Science and Technology Commission of Shanghai Municipality, China (under project No. 21511100302), and Natural Science Foundation of Shanghai (under project No. 22 ER1419000). It is also supported by The Research Project of Shanghai Science and Technology Commission (20 dz2260300) and The Fundamental Research Funds for the Central Universities.  
Not applicable.  
We conducted experiments on two benchmark datasets: MovieLens 1M (link: [[https://grouplens.org/datasets/movielens/1m](https://grouplens.org/datasets/movielens/1m)]([https://grouplens.org/datasets/movielens/1m](https://grouplens.org/datasets/movielens/1m)), accessed at 14 September 2021), and Tmall (link: [[https://tianchi.aliyun.com/dataset/dataDetail?dataId=53](https://tianchi.aliyun.com/dataset/dataDetail?dataId=53)]([https://tianchi.aliyun.com/dataset/dataDetail?dataId=53](https://tianchi.aliyun.com/dataset/dataDetail?dataId=53)), accessed at 3 January 2022).  
The authors declare no conflict of interest.