Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria

TL;DR

This paper introduces novel data-driven preference learning models, including a convex quadratic programming approach and a monotonic RNN, to improve sorting with temporal criteria, demonstrated through synthetic and real-world mobile gaming data.

Contribution

It proposes a new monotonic RNN model that captures evolving preferences over time while maintaining interpretability and scalability for multiple criteria sorting problems.

Findings

The models outperform baseline methods in synthetic data scenarios.

The proposed approaches achieve significant accuracy improvements in real-world user classification.

The monotonic RNN effectively models preference dynamics with interpretability.

Abstract

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.