mlsanityData to 14 Oct 2026

How Do Consumers Really Choose: Exposing Hidden Preferences with the Mixture of Experts Model

arXiv 2503.05800 ↗First posted 3 Mar 2025cs.LG econ.EMcited by 2

On the map: Mixture-of-experts → MoE theory and gating functions

Abstract (arXiv)

Understanding consumer choice is fundamental to marketing and management research, as firms increasingly seek to personalize offerings and optimize customer engagement. Traditional choice modeling frameworks, such as multinomial logit (MNL) and mixed logit models, impose rigid parametric assumptions that limit their ability to capture the complexity of consumer decision-making. This study introduces the Mixture of Experts (MoE) framework as a machine learning-driven alternative that dynamically segments consumers based on latent behavioral patterns. By leveraging probabilistic gating functions and specialized expert networks, MoE provides a flexible, nonparametric approach to modeling heterogeneous preferences. Empirical validation using large-scale retail data demonstrates that MoE significantly enhances predictive accuracy over traditional econometric models, capturing nonlinear consumer responses to price variations, brand preferences, and product attributes. The findings underscore MoEs potential to improve demand forecasting, optimize targeted marketing strategies, and refine segmentation practices. By offering a more granular and adaptive framework, this study bridges the gap between data-driven machine learning approaches and marketing theory, advocating for the integration of AI techniques in managerial decision-making and strategic consumer insights.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts. Builds on 7 of its 25 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

7 of 25 references.

References · 7

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2024A Survey on Mixture of Experts in Large Language Modelscited by 237
  3. 2024Multi-Head Mixture-of-Expertscited by 31
  4. 2022ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labelscited by 15
  5. 2024Multimodal Deep Learning of Word-of-Mouth Text and Demographics to Predict Customer Rating: Handling Consumer Heterogeneity in Marketingcited by 4
  6. 2024An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Marketcited by 4
  7. 2024A Dynamic Approach to Stock Price Prediction: Comparing RNN and Mixture of Experts Models Across Different Volatility Profilescited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026The Rise of Sparse Mixture-of-Experts: A Survey from Algorithmic Foundations to Decentralized Architectures and Vertical Domain Applicationscited by 2
  2. 2025Detecting Financial Fraud with Hybrid Deep Learning: A Mix-of-Experts Approach to Sequential and Anomalous Patternscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applications
  2. 2026Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention
  3. 2017An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
  4. 2026Cost-Aware Mixture-of-Experts Coordination for Model Markets
  5. 2026Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling
  6. 2026Large Behavior Model: A Promptable Digital Twin of the Retail Customer
  7. 2026A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning
  8. 2020Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint