Summary of A Primal-dual Online Learning Approach For Dynamic Pricing Of Sequentially Displayed Complementary Items Under Sale Constraints, by Francesco Emanuele Stradi et al.
A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints
by Francesco Emanuele Stradi, Filippo Cipriani, Lorenzo Ciampiconi, Marco Leonardi, Alessandro Rozza, Nicola Gatti
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 tackles the challenge of dynamically pricing complementary items that are sequentially displayed to customers, such as flight tickets with ancillary expenses like insurance. The authors argue that optimizing individual item prices is ineffective due to a lack of coherence in pricing policies. They formulate the problem as a Markov Decision Process (MDP) with constraints and design a primal-dual online optimization algorithm using online learning tools. The algorithm is evaluated using synthetic settings generated from real-world data, comparing its performance against baselines that optimize each state singularly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the best way to price things people buy online, like flight tickets, when there are extra costs like insurance or luggage fees. Right now, companies just pick prices without thinking about how they’ll affect each other. The authors wanted to change this by creating a new way of pricing that takes into account all the different items and makes sure they work together well. |
Keywords
* Artificial intelligence * Online learning * Optimization
