Summary of Misalignment, Learning, and Ranking: Harnessing Users Limited Attention, by Arpit Agarwal et al.
Misalignment, Learning, and Ranking: Harnessing Users Limited Attention
by Arpit Agarwal, Rad Niazadeh, Prathamesh Patil
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS)
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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 proposed model tackles the issue of users’ impulsive choices by utilizing their limited attention spans. The approach involves presenting items with unknown payoffs to a platform in a ranked list, and then allowing multiple users to select an item based on their preferences. This leads to the design of online bandit algorithms that can achieve vanishing regret against hindsight optimal benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re using a website or app that recommends things to you. Sometimes, you might choose something impulsively, without thinking about how it will affect the platform in the long run. This makes it hard for the platform to learn what items are actually valuable. A team of researchers came up with an idea to fix this problem by letting multiple people try out different options and see which ones they like best. By studying how well this approach works, they hope to create better algorithms that can help platforms make good decisions. |
Keywords
* Artificial intelligence * Attention
