DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
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Recent years have witnessed the progress of sequential recommendation in accurately predicting users' future behaviors. However, only persuading accuracy leads to the risk of filter bubbles where recommenders only focus on users' main interest areas. Different from other studies which improve diversity or coverage, we investigate the calibration in sequential recommendation, which aims to calibrate the interest distributions of recommendation lists and behavior sequences. However, existing calibration methods followed a post-processing paradigm, which costs more computation time and sacrifices the recommendation accuracy. To this end, we propose an end-to-end framework to provide both accurate and calibrated recommendations in sequential recommendation. We propose an objective function to measure the divergence of distributions between recommendation lists and historical behaviors. In addition, we design a decoupled-aggregated model which extracts information from two individual sequence encoders with different objectives to further improve the recommendation. Experiments on two benchmark datasets demonstrate the effectiveness and efficiency of our model.
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