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About this paper
Renzhe Xu, Xingxuan Zhang, Bo Li, Yafeng Zhang, Xiaolong Chen, Peng Cui
Researchers from Tsinghua University and Meituan Inc. developed a new consumer choice model for online product ranking, which incorporates stochastic attention span and purchase budget for multiple purchases. Their work introduces online learning algorithms with $\tilde{O}(\sqrt{T})$ regret guarantees and demonstrates empirically that these methods significantly improve revenue maximization compared to existing models.
Key insights
- Realistic consumer stopping behavior in multi-purchase settings is governed by a dynamic interplay of attention limits and purchase budget, requiring a model that captures both factors.
- The optimal product ranking in such an environment must account for how a product's purchase likelihood affects not only its own revenue but also the remaining opportunities for subsequent purchases within a session.
- Online learning algorithms based on the UCB principle can efficiently learn complex, personalized consumer parameters and translate them into revenue-maximizing ranking policies with strong theoretical guarantees.
Summary by alphaXiv.
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