用面板数据建模用户选择偏好，提升预测与商品组合优化效果
Estimation, Prediction, and Assortment Optimization for Markov Chain Choice Models with Panel Data
- 引入基于部分序偏好的马尔可夫链模型，捕捉同一用户历史交易依赖性
- 在合成数据和寿司数据集上，参数估计精度显著优于传统方法
- 首次系统研究条件选择与组合优化的计算复杂性，适合推荐系统研究者
我们提出一个基于面板数据的马尔可夫链（MC）选择模型框架，涵盖参数估计、个性化选择预测与个性化组合优化。与传统假设每次购买独立服从随机效用模型不同，本框架通过部分序偏好信息捕获同一用户历史交易间的依赖关系。据我们所知，这是首个在马尔可夫链框架下研究面板数据选择建模的工作。主要成果为提出新型期望最大化（EM）算法，融合部分序偏好信息进行参数估计；在合成数据集与寿司数据集上，其性能优于Simsek和Topaloglu（2018）的基准EM算法及Jagabathula和Vulcano（2018）基于多项式Logit的偏序基准。次要贡献为揭示条件选择预测与组合优化问题的计算难易性，明确其计算复杂性边界，对相关领域具有独立研究价值。
原文摘要 · Abstract (English)
We propose a framework for the Markov chain (MC) choice model with panel data, including parameter estimation, personalized choice prediction, and personalized assortment optimization. In contrast to the traditional setting, which assumes that each transaction is independently drawn from a random utility model, our framework accounts for dependencies among transactions for the same customer in historical data, captured by partial-ordering preference information. To the best of our knowledge, our framework initiates the study of choice modeling with panel data under MC. As our primary result, we propose novel expectation-maximization (EM) algorithms for MC parameter estimation by incorporating partial-ordering-based customer preference information. On synthetic datasets and the sushi dataset, our EM algorithms outperform the traditional EM algorithm of Simsek and Topaloglu (Operations Research, 66, 2018) and multinomial-logit-based partial-order benchmarks adapted from Jagabathula and Vulcano (Management Science, 64, 2018). As our secondary contribution, we present hardness and computational results for conditional choice prediction and assortment optimization problems. These results complement our estimation framework and clarify the computational landscape of conditional choice and assortment optimization, which may be of independent interest.
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