Understanding image quality and trust in peer-to-peer marketplaces
Research output: Contribution to journal › Conference article › Research › peer-review
As any savvy online shopper knows, second-hand peer-to-peer marketplaces are filled with images of mixed quality. How does image quality impact marketplace outcomes, and can quality be automatically predicted? In this work, we conducted a large-scale study on the quality of user-generated images in peer-to-peer marketplaces. By gathering a dataset of common second-hand products (˜75,000 images) and annotating a subset with human-labeled quality judgments, we were able to model and predict image quality with decent accuracy (˜87%). We then conducted two studies focused on understanding the relationship between these image quality scores and two marketplace outcomes: sales and perceived trustworthiness. We show that image quality is associated with higher likelihood that an item will be sold, though other factors such as view count were better predictors of sales. Nonetheless, we show that high quality user-generated images selected by our models outperform stock imagery in eliciting perceptions of trust from users. Our findings can inform the design of future marketplaces and guide potential sellers to take better product images.
Original language | English |
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Journal | Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 |
Pages (from-to) | 511-520 |
Number of pages | 10 |
DOIs | |
Publication status | Published - 4 Mar 2019 |
Externally published | Yes |
Event | 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 - Waikoloa Village, United States Duration: 7 Jan 2019 → 11 Jan 2019 |
Conference
Conference | 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019 |
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Country | United States |
City | Waikoloa Village |
Period | 07/01/2019 → 11/01/2019 |
Sponsor | IEEE Biometrics Council, IEEE Computer Society |
Bibliographical note
Funding Information:
This work is partly funded by a Facebook equipment donation to Cornell University and by AOL through the Connected Experiences Laboratory. We additionally wish to thank our crowd workers on Mechanical Turk and our colleagues from eBay.
Publisher Copyright:
© 2019 IEEE
ID: 301824730