Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria
This work addresses data-driven decision support for domains like mobile gaming by improving classification of users based on temporal behavioral data, though it appears incremental as it builds on existing MCS and deep learning approaches.
The study tackled the challenge of handling input time series data in multiple criteria sorting problems by developing novel preference learning methods, including a convex quadratic programming model and a monotonic Recurrent Neural Network (mRNN), which achieved notable performance improvements over baseline methods in synthetic and real-case scenarios.
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.