  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.
