发表机构
Melbourne Business School（墨尔本商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对具有面板数据的马尔可夫链选择模型，提出包含参数估计、选择预测及商品组合优化的框架。通过纳入偏序偏好信息的新颖期望最大化算法进行参数估计，在数据集上表现优于传统算法，还给出相关问题的难度与计算结果。
AI 中文摘要
我们提出了一个用于具有面板数据的马尔可夫链（MC）选择模型的框架，包括参数估计、个性化选择预测和个性化商品组合优化。与传统设定不同，传统设定假设每个交易独立地从随机效用模型中抽取，我们的框架考虑了历史数据中同一客户交易之间的依赖性，通过偏序偏好信息来捕捉。据我们所知，我们的框架开启了在MC下使用面板数据进行选择建模的研究。作为主要成果，我们通过纳入基于偏序的客户偏好信息，提出了用于MC参数估计的新颖期望最大化（EM）算法。在合成数据集和寿司数据集上，我们的EM算法优于Simsek和Topaloglu（《运筹学》，2018年，第66卷）的传统EM算法以及改编自Jagabathula和Vulcano（《管理科学》，2018年，第64卷）的基于多项逻辑的偏序基准。作为次要贡献，我们给出了条件选择预测和商品组合优化问题的难度和计算结果。这些结果补充了我们的估计框架，并阐明了条件选择和商品组合优化的计算格局，可能具有独立的研究价值。
英文摘要
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.
