# Learning Context-Dependent Choice Functions

## TL;DR

This paper introduces models for learning context-dependent choice functions using neural networks, addressing challenges like variable input size and order invariance, with extensive empirical validation on synthetic and real data.

## Contribution

It proposes a novel framework for modeling context-dependent preferences via utility functions and develops neural network architectures to learn these functions effectively.

## Key findings

- Neural network models outperform baselines on synthetic datasets.
- Models demonstrate strong generalization to real-world choice data.
- Approaches handle variable input sizes and order invariance effectively.

## Abstract

Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output. We study the problem of learning such functions under conditions of context-dependence of preferences, which means that the preference in favor of a certain choice alternative may depend on what other options are also available. In spite of its practical relevance, this kind of context-dependence has received little attention in preference learning so far. We propose a suitable model based on context-dependent (latent) utility functions, thereby reducing the problem to the task of learning such utility functions. Practically, this comes with a number of challenges. For example, the set of alternatives provided as input to a choice function can be of any size, and the output of the function should not depend on the order in which the alternatives are presented. To meet these requirements, we propose two general approaches based on two representations of context-dependent utility functions, as well as instantiations in the form of appropriate end-to-end trainable neural network architectures. Moreover, to demonstrate the performance of both networks, we present extensive empirical evaluations on both synthetic and real-world datasets.