# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 2 Related Work
### Calibrated Recommendation  
Existing sequential recommendation models achieved satisfactory recommendation accuracy. From the concerns of fairness and filter bubble, we focus on the calibration of recommendation lists of sequential recommendation algorithms. In recent years, calibration was proposed, which aimed to generate recommendation lists whose preference distributions were less divergent with the users' profile [17]. [PERSON] [17] also provided a post-processing greedy re-ranking model which considered both accuracy and calibration at each step of generating results. [PERSON] et al. [31] studied the connections between popularity bias and calibration. They found that users who were affected more by popularity bias tend to achieve less calibrated recommendation lists. [PERSON] and [PERSON] [18] compared intent-aware algorithms and calibration algorithms. They found that the diversity-oriented intent-aware models can achieve calibrated recommendations and calibration-oriented models can obtain diversity to some extent. [PERSON] et al. [20] proposed weighted total variation to measure the consistency between two distributions and a constrained optimization model to improve the ranking stage for calibration. [PERSON] et al. [21] proposed new metrics to evaluate calibrated recommendations and adaptive selection strategies for the trade-off weight in the post-processing algorithms.  
The calibration of recommendations is connected to two types of concepts. One is diversity, which aims to provide diversified recommendations for users. Seeking accuracy may lead to skewed recommendation lists which only focused on the main interest area of users, but users may be interested in diversified lists [17,29]. The calibrated recommendation constrained the recommendations to match the user's historical preference distribution to avoid the problem. However, it is a type of limited diversity because recommendations are limited by users' historical behaviors. Despite the limitation of interests, it is still considered as a solution of homogeneous contents [18,32]. Calibration is also a type of fairness. Fairness in recommender systems aimed to provide unbiased results for users. From the perspective of stakeholder, it can be defined as C-fairness, P-fairness and CP-fairness, which stand for consumers, providers, and the combination, respectively [19]. The calibrated recommender system can be treated as one of C-fairness [21]. The fairness is reflected by the less divergence of preference distributions between the user's profile and the recommendation list.  
Despite previous advances in calibrated recommendation, it still suffers from the following problems. First, existing methods for calibration required re-ranked the candidate items generated by a basic recommendation model, which required more time in generating recommendations. In addition, the post-processing models may sacrifice accuracy to improve calibration, because they separated the process of achieving the accuracy and calibration. In our work, we would like to explore whether calibrated recommendations can be provided in an end-to-end way without post-processing. In addition, we investigate whether considering both calibration and accuracy can contribute to the performance of sequential recommendation.