报告题目： A data-driven approach to modeling assortment optimization: The tractable case of similar substitutes
报告人： 上海财经大学江波教授
报告时间：2023年10月26日（星期四）下午3:00-4:00
报告地点：js金沙3983总站80602会议室
报告摘要：We propose a data-driven approach to model assortment optimization problems based on three real data sets. Our work is motivated by two empirical observations from customers browsing history on Taobao: one is that most customers browse very few items (≤ 5) before they make a purchase; the second is that there exists a sorting of items so that customer consideration sets are a small interval in the sorting. This algorithm sorting can be discovered by an algorithm due to Cuthill and McKee (1969). Based on these empirical observations, we build a framework for choice models, and show the connection between our framework and some popular choice models. To verify that models under our framework capture reality well, we use the dataset from Bodea et al. (2009) to fit different models and compare their performance on out-of-sample data. The result shows that our model provides a good balance between prediction accuracy and model complexity. Then, we consider the assortment optimization and pricing problem under our model and give fixed-parameter tractable algorithms for both problems. Finally, we implement our approach—going from data to modeling, and finally to optimization—on a third data set of customer clicking history on JD.com.
报告人简介：江波，上海财经大学信息管理与工程学院常聘教授，副院长；国家级青年人才、上海市高校特聘教授(东方学者)、上海市青年拔尖人才。从事运筹优化、收益管理、机器学习等方向的研究。成果发表于运筹优化与机器学习的国际顶级期刊《Operations Research》、《Mathematics of Operations Research》、《Mathematical Programming》、《Journal of Machine Learning Research》。为顺丰、京东、太平金科、永辉等多个国内著名企业提供无人仓库内优化、智能定价、智能选址、智能排班等技术服务。获得了中国运筹学会青年科技奖、上海市自然科学奖二等奖等荣誉。
