Dynamic Assortment Optimization in Live-Streaming Sales

This paper explores the dynamic assortment optimization problem in the context of live-streaming sales. In response to the dynamic characteristics of live-streaming e-commerce, we propose a new choice model that extends the traditional Multinomial Logit model into the continuous time domain. This novel model enables the decoupling of parameter estimation for different products and facilitates the convenient updating of posterior parameters at any time. Finally, we introduce a myopic optimization algorithm based on Thompson Sampling. This algorithm effectively balances exploration and exploitation, captures fluctuations in the number of audiences, and demonstrates superior numerical performance.