# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
###### Abstract  
Sequential recommendations have made great strides in accurately predicting the future behavior of users. However, seeking accuracy alone may bring side effects such as unfair and overspecialized recommendation results. In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity. On the one hand, it aims to provide fairer recommendations whose preference distributions are consistent with users' historical behaviors. On the other hand, it can improve the diversity of recommendations to a certain degree. But existing methods for calibration have mainly relied on the post-processing on the candidate lists, which require more computation time in generating recommendations. In addition, they fail to establish the relationship between accuracy and calibration, leading to the limitation of accuracy. To handle these problems, we propose an end-to-end framework to provide both accurate and calibrated recommendations for sequential recommendation. We design an objective function to calibrate the interests between recommendation lists and historical behaviors. We also provide distribution modification approaches to improve the diversity and mitigate the effect of imbalanced interests. In addition, we design a decoupled-aggregated model to improve the recommendation. The framework assigns two objectives to two individual sequence encoders, and aggregates the outputs by extracting useful information. Experiments on benchmark datasets validate the effectiveness of our proposed model. sequential recommendation; calibrated recommendation; fairness; diversity +
Footnote †: journal: Applied Sciences  
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which reflects the user's historical behaviors [17]. For example, if a user has watched 70% action movies and 30% comedies, a fully calibrated recommendation list should also contain action and comedy movies with this ratio. Compared to diversity, it can also provide diversified recommendation lists to a certain degree [18]. The difference between calibration and diversity is that calibration limits the covered item attributes of recommendations in the range of attributes interacted in the user's historical behaviors. Furthermore, if a user has homogeneous interests, the recommendations will become skewed under the constraint of calibration. From the perspective of fairness, it is a type of C-fairness according to the taxonomy proposed by [PERSON] et al. [19], which is the fairness from the consumer's perspective. For users with similar historical interests, the calibrated recommendation model is able to provide recommendation lists with similar interest distributions. This somehow avoids bias and reflects the fairness of the recommendation system. Existing studies in calibrated recommendations adopt a post-processing paradigm, which ranks items from a candidate list that has been generated by a basic recommendation model such as neural collaborative filtering [17; 20; 21]. This is different from the end-to-end recommendation paradigm of sequential recommendation. As illustrated in Figure 1, both end-to-end and post-processing-based models require predicted scores of all items. The difference is that the end-to-end models directly select the top-K items as final recommendations, while post-processing-based models apply a re-ranking stage on the top-Z (Z \(>\) K) items. For example, [PERSON] et al. [18] and [PERSON] et al. [21] generated calibrated recommendations based on the top-100 items provided by the basic algorithms. The advantage of post-processing-based approaches is that they can be applied to almost any recommendation model, because they only require the items and scores of the candidate list generated by the base model. However, the post-processing-based models have two limitations. On the one hand, these models do not consider the relation between accuracy and calibration. The post-processing-based models make calibrated predictions based on the trained basic models which are optimized by the accuracy objective.