  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.
