Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential Recommendation

TL;DR

This paper introduces LeapRec, a novel two-phase approach for calibrated sequential recommendation that balances relevance and calibration by disentangling these objectives during training and reranking.

Contribution

It proposes a calibration-disentangled learning-to-rank loss and a relevance-prioritized reranking strategy to improve calibration and relevance in sequential recommendations.

Findings

LeapRec outperforms previous methods on four real-world datasets.

The calibration-disentangled loss effectively balances relevance and calibration.

Relevance-prioritized reranking enhances user satisfaction in recommendations.

Abstract

Calibrated recommendation, which aims to maintain personalized proportions of categories within recommendations, is crucial in practical scenarios since it enhances user satisfaction by reflecting diverse interests. However, achieving calibration in a sequential setting (i.e., calibrated sequential recommendation) is challenging due to the need to adapt to users' evolving preferences. Previous methods typically leverage reranking algorithms to calibrate recommendations after training a model without considering the effect of calibration and do not effectively tackle the conflict between relevance and calibration during the reranking process. In this work, we propose LeapRec (Calibration-Disentangled Learning and Relevance-Prioritized Reranking), a novel approach for the calibrated sequential recommendation that addresses these challenges. LeapRec consists of two phases, model training phase and reranking phase. In the training phase, a backbone model is trained using our proposed calibration-disentangled learning-to-rank loss, which optimizes personalized rankings while integrating calibration considerations. In the reranking phase, relevant items are prioritized at the top of the list, with items needed for calibration following later to address potential conflicts between relevance and calibration. Through extensive experiments on four real-world datasets, we show that LeapRec consistently outperforms previous methods in the calibrated sequential recommendation. Our code is available at https://github.com/jeon185/LeapRec.