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. Additionally, we propose an ensemble learning algorithm designed to consolidate the outputs of multiple, potentially weaker, optimizers, a process executed efficiently through parallel computation. 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：预测方法的兴起推动了数据驱动决策支持在各领域的应用，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长期挑战。本研究提出了针对含时序准则的多准则排序问题的新型偏好学习方法。我们首先构建了一个在正则化框架下运行、具有固定时间折现因子的凸二次规划模型。此外，我们提出了一种集成学习算法，该算法通过并行计算高效整合多个潜在弱优化器的输出结果。为提升可扩展性并支持可学习时间折现因子，我们引入了一种新颖的单调循环神经网络（mRNN）。该网络旨在捕捉偏好随时间的动态演变，同时保持多准则排序问题固有特性（包括准则单调性、偏好独立性及类别的自然顺序）不变。所提出的mRNN可通过描绘边际价值函数与个性化时间折现因子随时间的变化来描述偏好动态，有效融合传统多准则排序方法的可解释性与深度偏好学习模型的预测潜力。我们通过合成数据场景及基于移动游戏应用中用户历史行为序列的优质用户分类真实案例研究，对所提模型进行了全面评估。实证结果表明，与涵盖机器学习、深度学习及传统多准则排序方法的一系列基线方法相比，所提模型在性能上取得了显著提升。