Summary of Predicting User Experience on Laptops From Hardware Specifications, by Saswat Padhi et al.
Predicting User Experience on Laptops from Hardware Specifications
by Saswat Padhi, Sunil K. Bhasin, Udaya K. Ammu, Alex Bergman, Allan Knies
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 addresses the challenge of estimating overall user experience (UX) on devices by proposing an innovative approach that moves beyond microbenchmark scores. By leveraging machine learning techniques and analyzing real-world consumer workloads, the authors aim to bridge the gap between manufacturers’ performance claims and actual user experiences. The proposed method can help device makers optimize their products for better UX, leading to increased customer satisfaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to guess how well a new smartphone will work based on just a few numbers that test its speed or memory. That’s not very helpful! This paper tries to solve this problem by creating a new way to measure how easy it is to use a device. They do this by looking at how people actually use devices in real life, rather than just testing one part of the device. This could help companies make better devices that are easier for us to use. |
Keywords
* Artificial intelligence * Machine learning
