Multiproduct Dynamic Pricing With Popularity Bias
研究了在线零售平台上两种流行度信息展示格式下，流行度偏差对多产品动态定价的影响，发现忽略该偏差会导致收益损失，并提出一种计算可行的半近视定价策略。
Popularity bias reflects the positive impact of popularity information on consumer choices. There are two common display formats for popularity information on online retail platforms: the total-based cumulative sales format, where the total sales of a product are displayed since its launch, and the period-based cumulative sales format, where only sales within a specific recent period are shown. Both types of popularity information are continuously updated. This paper focuses on the multiproduct dynamic pricing problem with popularity bias. We employ the widely used multinomial logit model to investigate the impact of popularity bias on consumer choices. In particular, we examine how popularity bias affects marginal revenue, pricing decisions, and market shares. Moreover, we highlight that ignoring popularity bias can lead to a suboptimal outcome. As the multiproduct dynamic pricing problem suffers from the curse of dimensionality, we propose a semi-myopic pricing policy, which is computationally tractable, and demonstrate its asymptotic optimality under both formats. Our numerical simulations further indicate that ignoring popularity bias can result in substantial revenue losses, while the semi-myopic pricing policy consistently outperforms other heuristics under both formats. Finally, empirical tests on real data provide a comprehensive procedure for identifying the most appropriate choice models, which offer practical insights for implementation.