讲座：Sequential Recommendation and Pricing under the Mixed Cascade Model 发布时间：2023-05-19
题 目：Sequential Recommendation and Pricing under the Mixed Cascade Model
主持人：孙海龙 助理教授 上海交通大学安泰经济与管理学院
Complementing previous research, we employ a mixed cascade model to describe consumers' sequential decision-making process in recommender systems. We evaluate the performance of our model using a real-world dataset and demonstrate its superior predictability compared to other multi-stage choice models. Due to the challenges of estimating the distribution of different cascade models, we investigate the assortment optimization problem when the distribution is unknown. Our analysis reveals that the optimal robust solution has a sequential revenue-ordered structure, which can be efficiently computed. Moreover, we show that this robust solution provides a performance guarantee compared to the scenario where the distribution is known, suggesting that the benefits of knowing more about consumers are limited. Finally, we study the joint assortment and pricing problem. Even though the joint problem is proven to be NP-hard, several constant-factor approximation algorithms are also proposed.
Gao Pin is currently an assistant professor at the School of Data Science, The Chinese University of Hong Kong, Shenzhen. He received a B.S. in physics from Wuhan University in 2013 and a M.Phil. in physics from Hong Kong University of Science and Technology (HKUST) in 2015, after which he worked in industry for two years. In 2021, he received a Ph.D. in Industry Engineering and Decision Analytics from HKUST. Dr. Gao's current research interests include revenue management and operations management.