# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
###### Abstract
In this scenario, accuracy and calibration are separate objectives, so that recommendation accuracy is limited by the trained basic model and considering calibration may sacrifice the accuracy. On the other hand, these models need more response time in generating recommendation lists because they have an extra ranking stage than the end-to-end models. During the ranking stage, the items of the final recommendation list are decided step by step, where the model needs to compute the gains of all candidates. Though the number of candidates can be much less than the size of the item set by selecting top-Z items, the ranking stage is still time-consuming. To handle these problems, we focus on providing accurate and calibrated results for sequential recommendation in an end-to-end framework. We propose a **D**ecoupled-**A**ggregated **C**alibrated **S**equential **R**ecommendation framework, namely \"**DACSR**\". First, we define a loss function for calibration to allow models can be optimized by accuracy and calibration simultaneously. We combine the prediction scores of all items and the item  
Figure 1: An illustration of end-to-end and post-processing-based recommendation. attribute information to estimate the distribution of the recommendation list. Then we use cosine similarity to measure the consistency between distributions of the recommendation list and behavior sequence. We also provide distribution modification approaches to further improve the diversity and mitigate the problem of amplification of main interests. Next, to handle the objectives of accuracy and calibration, we propose a decoupled-aggregated framework to provide accurate and calibrated recommendations. We utilize two individual sequence encoders which only focus on accuracy and calibration, respectively. Then we concatenate the item embeddings and sequence representations, and obtain final representations of the sequence and items by extractor networks with feed-forward networks and residual connections. The scores of all items are computed by the final representations, and the model is optimized according to the weighted sum of accuracy and calibration loss functions. The contributions of this paper are listed as follows:  
* We propose an end-to-end framework to provide accurate and calibrated recommendation lists for sequential recommendation. * We design a calibration loss function for model optimization, which aligns the preference distribution of recommendations to the historical distribution. In addition, we provide distribution modification methods for diversity and imbalanced interests. * We propose a decoupled-aggregated framework which aggregates information from individual sequence encoders which are optimized by calibration and accuracy separately. * Experiments on benchmark datasets show that our model can achieve accurate and calibrated recommendations, with less time consumption than post-processing-based models. The rest of this paper is organized as follows. We first review the existing literature in Section 2 and provide some preliminaries about sequential and calibrated recommendation in Sections 2 and 3. Then we introduce our model in Section 4. We provide experimental settings in Section 5 and show results and analysis in Section 6. Finally, we discuss our work in Section 7 and conclude our paper and indicate some future work in Section 8.