Name
Abstract | ||
|---|---|---|
Observational data gathering is expensive as it traditionally relies on human intervention and intuition. However, with advances in Artificial Intelligence (AI) machines are gaining the capacity in making sense of the unstructured data observational methods yield [1]. With these advances in technology there is a new push to create innovative ways of gathering observational data to give context to self-reported user feedback. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-92285-0_32 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Wi-Fi tracking,Machine learning,Customer Experience,Location-based tracking,Time-based tracking,Probe requests,Observational tracking | Observational study,Computer science,Intuition,Observational methods in psychology,Customer experience,Unstructured data,Human–computer interaction | Conference |
Volume | ISSN | Citations |
852 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Ebeling | 1 | 0 | 0.34 |
| Zach Luker | 2 | 0 | 0.34 |
| Seth Pacheco | 3 | 0 | 0.34 |
| Angela Payne | 4 | 0 | 0.34 |
| Nikki Rae | 5 | 0 | 0.34 |