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.

翻译：标定化推荐旨在保持推荐结果中各类别的个性化比例，在实际场景中至关重要，因为它通过反映用户的多样化兴趣来提升用户满意度。然而，在序列化场景中实现标定化（即标定化序列推荐）具有挑战性，因为需要适应用户不断演变的偏好。现有方法通常在训练模型后利用重排序算法进行标定，但未考虑标定过程对模型的影响，且未能有效解决重排序过程中相关性与标定化之间的冲突。本文提出LeapRec（校准解耦学习与相关性优先重排序），一种针对标定化序列推荐的新方法，以应对这些挑战。LeapRec包含模型训练和重排序两个阶段。在训练阶段，我们提出的校准解耦学习排序损失函数用于训练骨干模型，该损失函数在优化个性化排序的同时融入了标定化考量。在重排序阶段，相关性高的项目被优先置于列表顶部，而满足标定化需求的项目随后排列，以解决相关性与标定化之间的潜在冲突。通过在四个真实数据集上的大量实验，我们证明LeapRec在标定化序列推荐任务中 consistently outperforms 现有方法。代码已开源：https://github.com/jeon185/LeapRec。