Summary of Budgeted Recommendation with Delayed Feedback, by Kweiguu Liu and Setareh Maghsudi
Budgeted Recommendation with Delayed Feedback
by Kweiguu Liu, Setareh Maghsudi
First submitted to arxiv on: 19 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, researchers tackle the challenge of making decisions when you don’t get immediate feedback. This is crucial in many real-life situations where delays are common and resources are limited. The study focuses on the “exploration-exploitation dilemma” under these conditions. A motivating example is the distribution of medical supplies during the early stages of COVID-19, where delayed test results made it hard to optimize resource allocation. To address this issue, the researchers develop a decision-making policy called DORAL (Delay-Oriented Resource Allocation with Learning) that optimizes resource expenditure in contextual multi-armed bandits with arm-dependent delayed feedback. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making good decisions when you don’t get instant feedback. Imagine being a doctor trying to distribute medical supplies during an outbreak, but the test results take time to come back. This makes it hard to decide where to send what resources. The researchers created a new way of making these decisions that takes into account how long it takes to get feedback. |
