Demo Data from VLDB 2026 / reviewers in the wild / expert
Michael Smith 0024
dblp:44/5521-24
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 50% Recommender systems · 50% | |
| Artificial intelligence
1 paper |
Trustworthy machine learning · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems › content recommendation
document recommendation |
0.3 | 1 | 2017 | Quick Access: Building a Smart Experience for Google Drive · KDD 2017 |
Information retrieval
search engines |
0.3 | 1 | 2017 | Quick Access: Building a Smart Experience for Google Drive · KDD 2017 |
Machine learning › Trustworthy machine learning › privacy
privacy-preserving machine learning |
0.1 | 1 | 2017 | Quick Access: Building a Smart Experience for Google Drive · KDD 2017 |
Methods — techniques the papers use, named apart from their topics
applied machine learning · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Quick Access: Building a Smart Experience for Google DriveabstractGoogle 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. Sandeep Tata, Alexandrin Popescul, Marc Najork, Mike Colagrosso, Julian Gibbons, Alan Green, Alexandre Mah, Michael Smith 0024, Divanshu Garg, Cayden Meyer, Reuben Kan |
KDD | 8 |