DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

TL;DR

This paper introduces DACSR, an end-to-end framework for sequential recommendation that enhances fairness, diversity, and accuracy by calibrating recommendations with a decoupled-aggregated model and distribution modification techniques.

Contribution

It proposes a novel end-to-end calibration method with a decoupled-aggregated model, improving fairness, diversity, and accuracy in sequential recommendations.

Findings

Effective calibration of recommendations to user history.

Improved diversity and fairness in recommendations.

Validated on benchmark datasets with positive results.

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.