Summary of Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints, by Zifeng Zhao et al.
Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints
by Zifeng Zhao, Feiyu Jiang, Yi Yu
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper studies the contextual dynamic pricing problem, where a firm sells products to sequentially arriving consumers with unknown demand models. The goal is to maximize revenue by minimizing regret compared to a clairvoyant that knows the model ahead of time. A generalized linear model (GLM) is used to encode product and consumer information. Two algorithms are proposed: supCB and ETC, which achieve an optimal regret upper bound of order sqrt(dT), improving upon existing bounds by a sqrt(d) factor. The connection between dynamic pricing and contextual multi-armed bandits with many arms is highlighted. Additionally, the paper explores local differential privacy (LDP) constraints in dynamic pricing, proposing a stochastic gradient descent based ETC algorithm that achieves an optimal regret upper bound of order d*sqrt(T)/epsilon. Numerical experiments and a real data application on online lending demonstrate the efficiency and practical value of the proposed algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping companies sell products to people in a way that makes them happy and the company gets more money. It’s like trying to figure out what people want before they buy something, so you can make the right choices. The researchers came up with two new ways to do this: supCB and ETC. These methods are better than what was done before because they take into account lots of different factors. They also looked at how to keep customers’ information safe while still making good decisions. |
Keywords
» Artificial intelligence » Stochastic gradient descent
