Deep Context-Dependent Choice Model
Shuhan Zhang, Zhi Wang, Rui Gao, Shuang Li
Context-dependent choice behavior poses a fundamental challenge to traditional choice models, which often assume stable, independent utilities across alternatives. While recent context-dependent choice models attempt to capture such effects, they are typically limited by restrictive interaction structures or lack interpretability when scaled with flexible function approximators like neural networks. In this paper, we propose a novel modeling framework that bridges the gap between expressiveness and interpretability in context-dependent choice. We develop a neural architecture that decomposes utility into base, pairwise, and higher-order interaction components, with an inductive bias grounded in permutation-equivariant structure. Our framework supports feature-rich alternatives and enables systematic identification of interaction effects across different orders. We establish the universal approximation property of the model and demonstrate its empirical effectiveness on hypothetical, synthetic, and real-world datasets. The proposed approach achieves strong predictive performance while offering transparent insights into the contextual drivers of human choice.