DeepHalo: A Neural Choice Model with Controllable Context Effects
This work addresses the need for interpretable models of context effects in decision-making for applications like recommendation and human-AI alignment, representing an incremental improvement over existing methods.
The authors tackled the problem of modeling context-dependent human decision-making by proposing DeepHalo, a neural framework that enables explicit control over interaction order and interpretability, achieving strong predictive performance on synthetic and real-world datasets.
Modeling human decision-making is central to applications such as recommendation, preference learning, and human-AI alignment. While many classic models assume context-independent choice behavior, a large body of behavioral research shows that preferences are often influenced by the composition of the choice set itself -- a phenomenon known as the context effect or Halo effect. These effects can manifest as pairwise (first-order) or even higher-order interactions among the available alternatives. Recent models that attempt to capture such effects either focus on the featureless setting or, in the feature-based setting, rely on restrictive interaction structures or entangle interactions across all orders, which limits interpretability. In this work, we propose DeepHalo, a neural modeling framework that incorporates features while enabling explicit control over interaction order and principled interpretation of context effects. Our model enables systematic identification of interaction effects by order and serves as a universal approximator of context-dependent choice functions when specialized to a featureless setting. Experiments on synthetic and real-world datasets demonstrate strong predictive performance while providing greater transparency into the drivers of choice.