Summary of Learning From Streaming Data When Users Choose, by Jinyan Su et al.
Learning from Streaming Data when Users Choose
by Jinyan Su, Sarah Dean
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper formalizes the dynamics of digital markets where users choose between multiple service providers based on their preferences and user data is used to improve the service’s model. The service providers’ models influence the user’s choice, creating a feedback loop. A decentralized algorithm is developed to locally minimize the overall user loss, shown theoretically to converge asymptotically to stationary points almost surely. Experimental results demonstrate the utility of the algorithm using real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how people choose services in digital markets and how those choices affect what services are available. The goal is to find a way for users to make good choices without knowing everything about every service. Researchers came up with an idea called a decentralized algorithm that helps users choose the best option. They tested it using real data and showed that it works well. |
