The advent of predictive methodologies has catalyzed the emergence of data-driven decision support across various domains. However, developing models capable of effectively handling input time series data presents an enduring challenge. This study presents novel preference learning approaches to multiple criteria sorting problems in the presence of temporal criteria. We first formulate a convex quadratic programming model characterized by fixed time discount factors, operating within a regularization framework. To enhance scalability and accommodate learnable time discount factors, we introduce a novel monotonic Recurrent Neural Network (mRNN). It is designed to capture the evolving dynamics of preferences over time while upholding critical properties inherent to MCS problems, including criteria monotonicity, preference independence, and the natural ordering of classes. The proposed mRNN can describe the preference dynamics by depicting marginal value functions and personalized time discount factors along with time, effectively amalgamating the interpretability of traditional MCS methods with the predictive potential offered by deep preference learning models. Comprehensive assessments of the proposed models are conducted, encompassing synthetic data scenarios and a real-case study centered on classifying valuable users within a mobile gaming app based on their historical in-app behavioral sequences. Empirical findings underscore the notable performance improvements achieved by the proposed models when compared to a spectrum of baseline methods, spanning machine learning, deep learning, and conventional multiple criteria sorting approaches.

翻译：预测方法论的兴起推动了数据驱动决策支持在多个领域的发展。然而，开发能够有效处理输入时间序列数据的模型仍是一项持久挑战。本研究针对存在时间准则的多准则排序问题，提出了新颖的偏好学习方法。我们首先构建了一个具有固定时间折扣因子的凸二次规划模型，该模型在正则化框架下运行。为提升可扩展性并适应可学习的时间折扣因子，我们引入了一种新型单调循环神经网络（mRNN）。该网络旨在捕捉偏好的动态演化过程，同时保持多准则排序（MCS）问题所固有的关键性质，包括准则单调性、偏好独立性以及类别的自然排序。所提出的mRNN可通过描绘边际价值函数和随时间变化的个性化时间折扣因子来描述偏好动态，有效融合了传统MCS方法的可解释性与深度偏好学习模型的预测潜力。本研究对提出的模型进行了全面评估，涵盖合成数据场景以及一个真实案例研究——基于移动游戏应用中高价值用户的历史应用内行为序列对其进行分类。实证结果表明，与机器学习、深度学习及传统多准则排序方法等一系列基线方法相比，所提模型在性能上取得了显著提升。