Name
Abstract | ||
|---|---|---|
Google Drive is a cloud storage and collaboration service used by hundreds of millions of users around the world. Quick Access is a new feature in Google Drive that surfaces the most relevant documents when a user visits the home screen. Our metrics show that users locate their documents in half the time with this feature compared to previous approaches. The development of Quick Access illustrates many general challenges and constraints associated with practical machine learning such as protecting user privacy, working with data services that are not designed with machine learning in mind, and evolving product definitions. We believe that the lessons learned from this experience will be useful to practitioners tackling a wide range of applied machine learning problems. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3097983.3098048 | KDD |
Field | DocType | ISBN |
Data mining,World Wide Web,Computer science,Artificial intelligence,Artificial neural network,Data as a service,Machine learning,User privacy,Cloud storage | Conference | 978-1-4503-4887-4 |
Citations | PageRank | References |
7 | 0.44 | 15 |
Authors | ||
11 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandeep Tata | 1 | 478 | 27.50 |
| Alexandrin Popescul | 2 | 7 | 1.12 |
| Marc A. Najork | 3 | 2538 | 278.16 |
| Mike Colagrosso | 4 | 7 | 0.44 |
| Julian Gibbons | 5 | 7 | 0.44 |
| Alan Green | 6 | 7 | 0.44 |
| Alexandre Mah | 7 | 7 | 0.44 |
| Michael Smith | 8 | 87 | 12.62 |
| Divanshu Garg | 9 | 7 | 0.44 |
| Cayden Meyer | 10 | 7 | 0.44 |
| Reuben Kan | 11 | 7 | 0.44 |