用机器学习模型揭示消费者隐藏偏好，提升个性化推荐精准度。
How Do Consumers Really Choose: Exposing Hidden Preferences with the Mixture of Experts Model
- 引入专家混合模型动态划分消费者群体，捕捉隐性行为模式。
- 在真实零售数据上预测准确率显著高于传统经济模型。
- 适合做用户分群、精准营销和需求预测的管理者与研究者。
理解消费者选择是市场营销与管理研究的基础，企业日益追求个性化产品与客户互动优化。传统选择建模方法如多元逻辑回归（MNL）和混合逻辑回归模型依赖严格参数假设，难以刻画消费者决策的复杂性。本文提出基于机器学习的专家混合（Mixture of Experts, MoE）框架，通过概率门控函数与专用专家网络，实现对消费者偏好的灵活非参数建模。利用大规模零售数据进行实证验证表明，MoE显著提升预测精度，能有效捕捉价格变化、品牌偏好及产品属性带来的非线性响应。研究结果凸显其在需求预测、精准营销策略优化和细分实践中的潜力。该模型将数据驱动的机器学习与营销理论结合，推动人工智能技术在管理决策与消费者洞察中的融合应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。