# DeepHalo: A Neural Choice Model with Controllable Context Effects

arXiv: https://arxiv.org/abs/2601.04616 · HTML: https://mlsanity.com/paper/2601.04616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Discovering Context Effects from Raw Choice Data](https://mlsanity.com/paper/1902.03266/) (2019)
- [Predicting Choice with Set-Dependent Aggregation](https://mlsanity.com/paper/1906.06365/) (2019)
- [Representing Random Utility Choice Models with Neural Networks](https://mlsanity.com/paper/2207.12877/) (2022)
- [Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees](https://mlsanity.com/paper/2210.01282/) (2022)
- [Pairwise Choice Markov Chains](https://mlsanity.com/paper/1603.02740/) (2016)
- [Learning Interpretable Feature Context Effects in Discrete Choice](https://mlsanity.com/paper/2009.03417/) (2020)
- [Preference Modeling with Context-Dependent Salient Features](https://mlsanity.com/paper/2002.09615/) (2020)
- [Learning Rich Rankings](https://mlsanity.com/paper/2312.15081/) (2023)
- [Learning Context-Dependent Choice Functions](https://mlsanity.com/paper/1901.10860/) (2019)
- [Graph-Based Methods for Discrete Choice](https://mlsanity.com/paper/2205.11365/) (2022)
- [Transformer Choice Net: A Transformer Neural Network for Choice Prediction](https://mlsanity.com/paper/2310.08716/) (2023)
- [Choice Set Optimization Under Discrete Choice Models of Group Decisions](https://mlsanity.com/paper/2002.00421/) (2020)
- [On the estimation of discrete choice models to capture irrational customer behaviors](https://mlsanity.com/paper/2109.03882/) (2021)
- [Modeling Choice via Self-Attention](https://mlsanity.com/paper/2311.07607/) (2023)
- [Modeling Reference-dependent Choices with Graph Neural Networks](https://mlsanity.com/paper/2408.11302/) (2024)

## Built on by (most cited first, 1 of 1)

- [Consideration Circuits: Depth Separation and Universality Beyond a Single Softmax](https://mlsanity.com/paper/2610.04143/) (2026)

## Abstract (arXiv)

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.