CPMetric: Deep Siamese Networks for Learning Distances Between Structured Preferences

TL;DR

This paper introduces CPDist, a deep Siamese network model that effectively learns to measure distances between structured preferences represented by CP-nets, outperforming existing methods in accuracy and efficiency.

Contribution

The paper presents CPDist, a novel neural network approach for learning complex preference distance metrics directly from data, improving accuracy and computational efficiency over existing algorithms.

Findings

CPDist accurately learns preference distances with high precision.

CPDist outperforms existing approximation algorithms in speed and accuracy.

The model generalizes well even with limited training samples.

Abstract

Preference are central to decision making by both machines and humans. Representing, learning, and reasoning with preferences is an important area of study both within computer science and across the sciences. When working with preferences it is necessary to understand and compute the distance between sets of objects, e.g., the preferences of a user and a the descriptions of objects to be recommended. We present CPDist, a novel neural network to address the problem of learning to measure the distance between structured preference representations. We use the popular CP-net formalism to represent preferences and then leverage deep neural networks to learn a recently proposed metric function that is computationally hard to compute directly. CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations. We find that CPDist is able to learn the distance function with high accuracy and outperform existing approximation algorithms on both the regression and classification task using less computation time. Performance remains good even when CPDist is trained with only a small number of samples compared to the dimension of the solution space, indicating the network generalizes well.