Context-Based Dynamic Pricing with Online Clustering

Miao, Sentao; Chen, Xi; Chao, Xiuli; Liu, Jiaxi; Zhang, Yidong

doi:10.48550/arxiv.1902.06199

Cited by 5 publications

(9 citation statements)

References 34 publications

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“…In addition to the regret upper bound for the general personalized information setting, we also study a "stochastic" setting in which the customers' personal information {x t } is assumed to be stochastic and independently and identically distributed from an unknown non-degenerate distribution. We remark that this is a common assumption/setting studied in the existing literature (Qiang & Bayati 2016, Miao et al 2019. In this setting, with some changes of hyperparameters of our proposed algorithm, an improved regret upper bound of O(d…”

Section: Our Contributionsmentioning

confidence: 82%

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

Chen

Simchi‐Levi

2020

Preprint

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The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over T time periods with an unknown demand function of posted price and personalized information. At each time t, the retailer observes an arriving customer's personal information and offers a price.The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. To this end, we first introduce a notion of anticipating (ε, δ)-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for d-dimensional personalized information, our algorithm achieves the expected regret at the order of O(ε −1 √ d 3 T ), when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to O( √ d 2 T + ε −2 d 2 ).

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Section: Our Contributionsmentioning

confidence: 82%

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

Chen

Simchi‐Levi

2020

Preprint

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“…However, in modern retailing industries, such as fast fashion, the underlying demand function cannot be easily estimated from historical data. This motivates a body of research on dynamic pricing with demand learning (see, e.g., Araman & Caldentey (2009), Besbes & Zeevi (2009), Farias & Van Roy (2010), Broder & Rusmevichientong (2012), Harrison et al (2012), den Boer & Zwart (2013, Keskin & Zeevi (2014), Wang et al (2014), Lei et al (2014), Chen et al (2015), Miao et al (2019), Wang et al (2021) and references therein).…”

Section: Related Workmentioning

confidence: 98%

Fairness-aware Online Price Discrimination with Nonparametric Demand Models

Chen

Zhang

Zhou

2021

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Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concern in fairness, which even violates the regulation and law. This paper studies the problem of dynamic discriminatory pricing under fairness constraints. In particular, we consider a finite selling horizon of length T for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. For each selling period, the seller determines the price for each group and observes their purchase behavior. While existing literature mainly focuses on maximizing revenue, ensuring fairness among different customers has not been fully explored in the dynamic pricing literature. In this work, we adopt the fairness notion from (Cohen et al. 2021a). For price fairness, we propose an optimal dynamic pricing policy in terms of regret, which enforces the strict price fairness constraint. In contrast to the standard √ T -type regret in online learning, we show that the optimal regret in our case is Θ(T 4/5 ). We further extend our algorithm to a more general notion of fairness, which includes demand fairness as a special case. To handle this general class, we propose a soft fairness constraint and develop the dynamic pricing policy that achieves O(T 4/5 ) regret.

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“…We stress that the RL setting has unique challenges so methods for the bandit setting do not directly extend. Among others, Miao et al (2019) is the closest to our paper, in the sense that they also treat data from different populations in a model-free manner. Based on a semi-myopic pricing policy, Miao et al (2019) propose an adaptive clustering method to pool data across different customer classes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Among others, Miao et al (2019) is the closest to our paper, in the sense that they also treat data from different populations in a model-free manner. Based on a semi-myopic pricing policy, Miao et al (2019) propose an adaptive clustering method to pool data across different customer classes. We make a detailed comparison with their method in Sections 6.2 and 6.5, showing the advantages of our algorithm in overcoming the three challenges discussed in Section 1.1.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-pooling Reinforcement Learning for Personalized Healthcare Intervention

Shi¹,

Pu²

2022

Preprint

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Motivated by the emerging needs of personalized preventative intervention in many healthcare applications, we consider a multi-stage, dynamic decision-making problem in the online setting with unknown model parameters. To deal with the pervasive issue of small sample size in personalized planning, we develop a novel data-pooling reinforcement learning (RL) algorithm based on a general perturbed value iteration framework. Our algorithm adaptively pools historical data, with three main innovations: (i) the weight of pooling ties directly to the performance of decision (measured by regret) as opposed to estimation accuracy in conventional methods; (ii) no parametric assumptions are needed between historical and current data; and (iii) requiring data-sharing only via aggregate statistics, as opposed to patient-level data. Our datapooling algorithm framework applies to a variety of popular RL algorithms, and we establish a theoretical performance guarantee showing that our pooling version achieves a regret bound strictly smaller than that of the no-pooling counterpart. We substantiate the theoretical development with empirically better performance of our algorithm via a case study in the context of post-discharge intervention to prevent unplanned readmissions, generating practical insights for healthcare management. In particular, our algorithm alleviates privacy concerns about sharing health data, which (i) opens the door for individual organizations to levering public datasets or published studies to better manage their own patients; and (ii) provides the basis for public policy makers to encourage organizations to share aggregate data to improve population health outcomes for the broader community.

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Context-Based Dynamic Pricing with Online Clustering

Cited by 5 publications

References 34 publications

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning

Fairness-aware Online Price Discrimination with Nonparametric Demand Models

Data-pooling Reinforcement Learning for Personalized Healthcare Intervention

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