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# awesome-dynamic-pricing [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

Everything about intelligent pricing(dynamic pricing/algorithmic pricing) in the fields of management science, operations research, computer science and artificial intelligence.

# Table of Contents

- [Institution List](#institution-list) 
- [Scholar List](#scholar-list) 
- [Journal and Conference List](#journal-and-conference-list)
- [Blog and Tutorial List](#blog-and-tutorial-list) 
- [Book List](#book-list)
- [Survey List](#survey-list)
- [Paper List](#paper-list) 
  - [Technology Perspective](#technology-perspective)
    - [Game Theory](#game-theory)
    - [Operational Research](#operational-research)
    - [Demand Learning](#demand-learning)
    - [Reinforcement Learning](#reinforcement-learning)
  - [Application Perspective](#application-perspective)
    - [Retail](#retail)
    - [Transportation](#transportation)
    - [Gird](#smart-gird)
    - [Service](#internet-service)
    - [Auction](#auction)
    - [Hotel](#hotel)
- [Paper Comments](#paper-comments)

# Institution List
1. INFORMS, The Institute for Operations Research and the Management Sciences. [INFORMS Journals](https://www.informs.org/Publications/INFORMS-Journals)
2. IFORS, The International Federation of Operational Research Societies.
3. SIAM, Society for Industrial and Applied Mathematics.

# Scholar List
1. Robert L. Phillips, Uber, Columbia University. [WIKI](https://en.wikipedia.org/wiki/Robert_L._Phillips)
2. N. Bora Keskin, Duke University. [WIKI](https://people.duke.edu/~nk145/)
3. Yiwei Chen, MIT PhD, Temple University. [WIKI](https://www.fox.temple.edu/about-fox/directory/yiwei-chen/)
4. Vivek F. Farias, MIT Sloan School of Management. [WIKI](https://mitsloan.mit.edu/faculty/directory/vivek-f-farias)
5. Victor F. ARAMAN, Stanford PhD, New York University, American University of Beirut. [WIKI](https://pages.stern.nyu.edu/~varaman/)
6. René Caldentey, The University of Chicago, NYU, MIT Sloan School of Management. [WIKI](https://www.chicagobooth.edu/faculty/directory/c/rene-caldentey#)

# Journal and Conference List

## Operations Research & Mangement Science
### Journal
1. Management Science
2. Operations Research
3. Mathematical Programming
4. Journal of Operations Management
5. Manufacturing & Service Operations Management
6. Production and Operations Management
7. Computers & Operations Research
8. European Journal of Operational Research
9. INFORMS Journal on Computing
10. Transportation Science
11. Marketing Science

### Conference
1. ICORES, International Conference on Operations Research and Enterprise Systems
2. COCOA, International Conference on Combinatorial Optimization and Applications

## Computer Science & Artificial Intelligence
### Conference
1. AAAI, AAAI Conference on Artificial Intelligence
2. IJCAI, International Joint Conference on Artificial Intelligence
3. NeurIPS, Conference on Neural Information Processing Systems
4. KDD, ACM SIGKDD Conference on Knowledge Discovery and Data Mining
5. ICML, International Conference on Machine Learning

# Blog and Tutorial List
1. Deep Reinforcement Learning for Supply Chain and Price Optimization, [LINK](https://blog.griddynamics.com/deep-reinforcement-learning-for-supply-chain-and-price-optimization/)
2. Thompson Sampling of Multi-Armed Bandits, [LINK](https://towardsdatascience.com/thompson-sampling-fc28817eacb8)

# Book List

1. Pricing and Revenue Optimization, Phillips, Robert Lewis, Stanford University, 2005.
2. The Theory and Practice of Revenue Management, Kalyan T. Talluri, Columbia University, 2006.
3. Pricing strategy: Setting Price Levels, Managing Price Discounts and Establishing Price Structures, Keast, Sarah, 2013.
4. Introduction to Algorithmic Marketing, Ilya Katsov, Grid Dynamics, 2018
5. Price Management: Strategy, Analysis, Decision, Implementation, Hermann Simon, 2019
6. Algorithms for Decision Making, Mykel J. Kochenderfer and TIM A. Wheeler MIT Press, 2022. 
7. Global Supply Chain and Operations Management：A Decision-Oriented Introduction to the Creation of Value, D Ivanov, 2019.
8. The Oxford Handbook of Pricing Management, Oxford University Press, 2012.

# Survey List
1. Recent Developments in Dynamic Pricing Research：Multiple Products, Competition and Limited Demand Information, Production and Operations Management, 2015.
2. Modeling Elasticity: A Brief Survey of Price Elasticity of Demand Estimation Methods, Journal of Research in Marketing, 2019.
3. An Overview of Pricing Models for Revenue Management, Manufacturing & Service Operations Management, 2003.
4. Why Prices Need Algorithms, Stanford University, IJCAI, 2016.
5. From Predictive to Prescriptive Analytics, Mangagement Science, 2019.
6. Reinforcement Learning for Ridesharing: An Extended Survey. Transportation Research Part C, Lyft, 2021.

# Paper List

## Technology Perspective 
### Game Theory
1. Game Theory Model of Pricing, Handbook of Pricing Management, Oxford University Press, 2012.
2. More Amazon Effects: Online Competition and Pricing Behaviors, Harvard Business School and NBER, 2018.
3. Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach, KDD, 2018.
4. Dynamic Pricing for Reusable Resources in Competitive Market With Stochastic Demand, AAAI, 2018.
5. The Competitive Effects of Variance-based Pricing, IJCAI, 2020.

### Operational Research
1. Data-Driven Inventory Management and Dynamic Pricing Competition on Online Marketplaces, IJCAI, 2018.
2. Clearance Pricing Optimization for a Fast-Fashion Retailer, Operations Research, 2012.
3. Dynamic Pricing and Lot Sizing for a Newsvendor Problem with Supplier Selection, Quantity Discounts, and Limited Supply Capacity, Computers & Industrial Engineering, 2021.
4. Bundle Pricing and Inventory Decisions on Complementary Products, Operational Research, 2017.
5. Optimization Beyond Prediction: Prescriptive Price Optimization, NEC Corporation,  KDD, 2017. [Comments 2017-KDD-OBP](#2017-KDD-OBP)

### Demand Learning
1. Dynamic Pricing for Nonperishable Products with Demand Learning, Operations Research, 2009.
2. Analytics for an Online Retailer: Demand Forecasting and Price Optimization. INFORMS, 2015.
3. Dynamic Pricing with Bayesian Demand Learning and Reference Price Effect, European Journal of Operational Research, 2019.
4. Context-Based Dynamic Pricing with Online Clustering, University of Michigan and Alibaba, Management Science, 2019. (Reviewing).
5. Dynamic Pricing for Reusable Resources in Competitive Market With Stochastic Demand, AAAI, 2018.
6. Price Investment using Prescriptive Analytics and Optimization in Retail. Walmart Labs, KDD, 2020.
7. Deep Censored Learning of the Winning Price in the Real Time Bidding, KDD, 2018.
8. Robust Quadratic Programming for Price Optimization, NEC Corporation, IJCAI, 2017.
9. Mixed **Causal** Structure Discovery with Application to Prescriptive Pricing, IJCAI, NEC Labs China, IJCAI, 2018.
10. Coordinating Pricing and Inventory Replenishment with Nonparametric Demand Learning, Operations Research, 2019.
11. Dynamic Pricing and Learning under the Bass Model, arXiv, 2021. (**Good Survey**)
12. A Simple Rule for Pricing with Limited Knowledge of Demand, Management Science, 2021.
13. Discontinuous Demand Functions: Estimation and Pricing, Management Science, 2020.
14. Efficient Algorithms for the Dynamic Pricing Problem with Reference Price Effect, Management Science, 2016.
15. On the (Surprising) Sufficiency of Linear Models for Dynamic Pricing with Demand Learning, Management Science, 2015.
16. Demand Models for the Static Retail Price Optimization Problem – A Revenue Management Perspective, SCOR, 2014.

### Reinforcement Learning
#### MAB
1. Semi-Parametric Dynamic Contextual Pricing, Stanford Univisersity, NIPS, 2019.
2. Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing, MIT and Microsoft, NIPS, 2019.
3. Online Pricing for Revenue Maximization with Unknown Time Discounting Valuations, IJCAI, 2018.
4. Thompson Sampling for Dynamic Pricing, Walmart Labs, 2018.
5. Online Second Price Auction with Semi-Bandit Feedback under the Non-Stationary Setting, Microsoft, AAAI, 2020.
6. The Promise and Perils of Myopia in Dynamic Pricing With Censored Information, IJCAI, 2018.
7. Optimal Posted-Price Mechanism in Microtask Crowdsourcing, IJCAI, 2019.
8. Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes, NIPS, 2020.
9. Contextual Pricing for Lipschitz Buyers, NIPS, 2018.
10. Online Pricing with Strategic and Patient Buyer, MSR, NIPS, 2016.
11. Correlated Bandits for Dynamic Pricing via the ARC algorithm, arXiv, 2021.
12. Dynamic Online Pricing with Incomplete Information Using Multiarmed Bandit Experiments, Marketing Science, 2019.
13. Online Learning and Pricing with Reusable Resources: Linear Bandits with Sub-Exponential Rewards, ICML, 2022.

#### DRL
1. Dynamic Pricing on E-commerce Platform with Deep Reinforcement  Learning, Alibaba, 2019.
2. Reinforcement Mechanism Design, with Applications to Dynamic Pricing in Sponsored Search Auctions, Baidu, AAAI, 2020.
3. Learning Resource Allocation and Pricing for Cloud Profit Maximization, AAAI, 2019.
4. Latent Dirichlet Allocation for Internet Price War. Ant Finacial Services, AAAI, 2019.
5. Online Revenue Maximization for Server Pricing, IJCAI, 2020.
6. Dispatching Through Pricing: Modeling Ride-Sharing and Designing Dynamic Prices, IJCAI, 2019.

#### Feature-based Models
1. Dynamic Pricing in High-dimensions, The Journal of Machine Learning Research, 2019
2. Logarithmic Regret in Feature-based Dynamic Pricing, arXiv, 2021.
3. Feature-Based Dynamic Pricing, Management Science, 2020.
4. Logarithmic Regret in Feature-based Dynamic Pricing, NIPS, 2022. (**Good Survey**)

## Application Perspective
### Retail
1. Anomaly Detection for an E-commerce Pricing System, Walmart Labs, KDD, 2019.
2. Improving Lives of Indebted Farmers Using Deep Learning: Predicting Agricultural Produce Prices Using Convolutional Neural Networks. Wenzhou Kean University and Pennsylvania State University. AAAI, 2020.
3. Price-aware Recommendation with Graph Convolutional Networks, ICDE, 2020.
4. Analytics for an Online Retailer: Demand Forecasting and Price Optimization. INFORMS, 2015.
5. Price Investment using Prescriptive Analytics and Optimization in Retail. Walmart Labs, KDD, 2020.
6. Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs, WWW, 2017.
7. Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data, ICML, 2014.
8. Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments, Management Science, 2018.

### Transportation
1. Pay Your Trip for Traffic Congestion- Dynamic Pricing in Traffic-Aware Road Networks, AAAI, 2020.
2. Optimal Pricing for Distance-Based Transit Fares, University of Southern California, AAAI, 2018.
3. Dynamic Pricing for Airline Ancillaries with Customer Context, KDD, 2019.
4. Dispatching Through Pricing: Modeling Ride-Sharing and Designing Dynamic Prices, IJCAI, 2019.
5. Price-and-Time-Aware Dynamic Ridesharing, ICDE, 2018.
6. InBEDE: Integrating Contextual Bandit with TD Learning for Joint Pricing and Dispatch of Ride-Hailing Platforms, ICDM, 2019.
7. Your Uber Is Arriving: Managing On-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives, Management Science, 2019.
8. Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms, IJCAI, 2022.

### Smart Gird
1. Aggregate Demand-Based Real-Time Pricing Mechanism for the Smart Grid: A Game-Theoretic Analysis, IBM Research, IJCAI, 2015
2. Approximately Stable Pricing for Coordinated Purchasing of Electricity, University of Toronto, IJCAI, 2015.

### Internet Service[Ads, Cloud, Finance, Search]
1. Learning Resource Allocation and Pricing for Cloud Profit Maximization, AAAI, 2019.
2. Price Forecast with High-Frequency Finance Data: An Autoregressive Recurrent Neural Network Model with Technical Indicators, CIKM, 2020.
3. Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News, CIKM, 2018.
4. Incorporating Corporation Relationship via Graph Convolutional Neural Networks for Stock Price Prediction, CIKM, 2018.
5. Online Search With Best-Price and Query-Based Predictions, AAAI, 2022.

### Auction
1. Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer, NIPS, 2019.
2. Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions, NIPS, 2019.
3. Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising, KDD, 2019
4. Learning Optimal Reserve Price against Non-myopic Bidders, NIPS, 2018.
5. Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming, NIPS, 2020.
6. Horizon-Independent Optimal Pricing in Repeated Auctions with Truthful and Strategic Buyers, WWW, 2017.
7. A Field Guide to Personalized Reserve Prices, WWW, 2016.
8. Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve, WWW, 2016.
9. Bid Shading in The Brave New World of First-Price Auctions, CIKM, 2020.
10. Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer, ICML, 2018.

### Hotel
1. Customized Regression Model for Airbnb Dynamic Pricing, KDD, 2018.
2. Deep Learning based Dynamic Pricing Model for Hotel Revenue Management, ICDM, 2019.
3. Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing, Alibaba, 2022.

### Others
1. No-regret Learning in Price Competitions under Consumer Reference Effects, MIT, NIPS, 2020.
2. Large-Scale Price Optimization via Network Flow, NEC Corporation, NIPS, 2016.
3. Skimming or Penetration? Strategic Dynamic Pricing for New Products, Marketing Science, 2015.

# Paper Comments
## 2017-KDD-OBP: 
  [*Optimization Beyond Prediction: Prescriptive Price Optimization*]: The prescriptive price optimization consists of prediction modeling and price optimization. To reduce the exponential computation complexity, the paper formulize the price optimization problem to the form the binary quatratic programming (BQP), and then relax the BQP to the form of mixed integer linear programming (MILP) and semidefinite programming (SDP).

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