DACSR: Dual-Aggregation End-to-End Calibrated Sequential Recommendation

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 improves diversity or coverage, we investigate the calibration in sequential recommendation. We propose an end-to-end framework to provide both accurate and calibrated recommendations. We propose a loss function for sequential recommendation framework. In addition, we design a dual-aggregation model to further improve the recommendation. Experiments on two benchmark datasets demonstrate the effectiveness and efficiency of our model.