Summary of Using Deep Learning to Enhance Electronic Service Quality: Application to Real Estate Websites, by Samaa Elnagar
Using deep learning to enhance electronic service quality: Application to real estate websites
by Samaa Elnagar
First submitted to arxiv on: 27 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 research focuses on improving the quality of electronic services by incorporating visual and descriptive features. The authors highlight that existing services often overlook tangibility, which can be balanced by incorporating visuals or tangible tools. They leverage advancements in Deep Learning for computer vision to enhance browsing and searching experiences. A key finding is the importance of integrating visual and descriptive features to improve e-service efficiency and tangibility. The paper introduces a novel feature, Damage Level, which uses Mask-RCNN to estimate damage in real estate images. This feature is incorporated into an electronic real estate service model aimed at enhancing customer experience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Electronic services often struggle with quality, but this research shows how visual and descriptive features can make them better. Imagine searching for a house online and being able to see if it has any damage. That’s what this paper is all about – making online services more helpful by adding pictures and details. The authors use special computer learning tools to help people find the right property quickly and easily. They even create a new way to measure property damage, called Damage Level. This can make a big difference in how customers feel when they’re searching for homes online. |
Keywords
» Artificial intelligence » Deep learning » Mask » Rcnn
