From Counts to Preferences: Preference-Driven Models for Spatio-Temporal Event Data

Chao Yang, Yiling Kuang, Shuang Li

TL;DR: We bridge choice theory and deep learning to model human-generated spatiotemporal events. Our framework explicitly mimics a two-stage "consider-then-choose" decision process using a sparse gating network and a learnable utility function.

Abstract: Spatio-temporal event data---such as crime incidents or shared-mobility usage---are generated by human decisions. Yet most existing models focus on statistical dependencies in time and space, overlooking the cognitive and social factors that shape behavior. We argue that uncovering underlying preferences is essential, as they provide a structured link between observed event data and decision processes. We introduce a preference-driven framework that models event distributions through a two-stage ``consider--then--choose'' process: sparse gating captures limited attention, and utility functions guide selection within the consideration set. To capture heterogeneity, we employ a mixture-of-experts design that reveals distinct preference patterns across groups and contexts. The framework incorporates sparse structural design, and we analyze its theoretical properties by establishing approximation and generalization guarantees. Empirical studies on crime and bike-sharing datasets demonstrate competitive predictive accuracy while providing interpretable insights into behavioral drivers. By shifting the focus from counts to preferences, our approach offers a behaviorally grounded and socially meaningful perspective for modeling event data.

Submission Number: 2004