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Cayden Meyer

dblp:204/3404 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Recommender systems › content recommendation
document recommendation
0.312017
Quick Access: Building a Smart Experience for Google Drive · KDD 2017
Information retrieval
search engines
0.312017
Quick Access: Building a Smart Experience for Google Drive · KDD 2017
Machine learning › Trustworthy machine learning › privacy
privacy-preserving machine learning
0.112017
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
YearPublicationVenuePosition
2017 Quick Access: Building a Smart Experience for Google Drive
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.
Sandeep Tata, Alexandrin Popescul, Marc Najork, Mike Colagrosso, Julian Gibbons, Alan Green, Alexandre Mah, Michael Smith 0024, Divanshu Garg, Cayden Meyer, Reuben Kan
KDD10