Abstract
People capture photos, audio recordings, video, and more on a daily basis, but organizing all these digital artifacts quickly becomes a daunting task. Automated solutions struggle to help us manage this data because they cannot understand its meaning. In this paper, we introduce Kurator, a hybrid intelligence system leveraging mixed-expertise crowds to help families curate their personal digital content. Kurator produces a refined set of content via a combination of automated systems able to scale to large data sets and human crowds able to understand the data. Our results with 5 families show that Kurator can reduce the amount of effort needed to find meaningful memories within a large collection. This work also suggests that crowdsourcing can be used effectively even in domains where personal preference is key to accurately solving the task.
| Original language | English (US) |
|---|---|
| Title of host publication | CSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing |
| Publisher | Association for Computing Machinery |
| Pages | 1835-1849 |
| Number of pages | 15 |
| ISBN (Electronic) | 9781450343350 |
| DOIs | |
| State | Published - Feb 25 2017 |
| Event | 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017 - Portland, United States Duration: Feb 25 2017 → Mar 1 2017 |
Publication series
| Name | Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW |
|---|
Other
| Other | 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017 |
|---|---|
| Country/Territory | United States |
| City | Portland |
| Period | 2/25/17 → 3/1/17 |
Bibliographical note
Publisher Copyright:© 2017 ACM.
Keywords
- Crowdsourcing
- Digital audio
- Digital curation
- Hybrid intelligence
- Mixed-expertise
- Personal curation