Thomas Mahoney

dblp:150/2354 · DBLP profile ↗
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3ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1Theory of computation · 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
2 papers
Data mining · 64% Knowledge graphs · 19% Web and social media mining · 17%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 2 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge graphs
knowledge base linking
0.112018
Lessons Learned from Developing and Deploying a Large-Scale Employer Name Normalization System for Online Recruitment · KDD 2018
Web and social media mining
online recruitment
0.112017
Large-Scale Occupational Skills Normalization for Online Recruitment · AAAI 2017

Methods — techniques the papers use, named apart from their topics

machine learning · 0.3entity linking · 0.3
YearPublicationVenuePosition
2018 Lessons Learned from Developing and Deploying a Large-Scale Employer Name Normalization System for Online Recruitment
abstract
Employer name normalization, or linking employer names in job postings or resumes to entities in an employer knowledge base (KB), is important for many downstream applications in the online recruitment domain. Key challenges for employer name normalization include handling employer names from both job postings and resumes, leveraging the corresponding location and URL context, and handling name variations and duplicates in the KB. In this paper, we describe the CompanyDepot system developed at CareerBuilder, which uses machine learning techniques to address these challenges. We discuss the main challenges and share our lessons learned in deployment, maintenance, and utilization of the system over the past two years. We also share several examples of how the system has been used in applications at CareerBuilder to deliver value to end customers.
Qiaoling Liu, Josh Chao, Thomas Mahoney, Alan Chern, Chris Min, Faizan Javed, Valentin Jijkoun
KDD3
2018 Dynamic coloring parameters for graphs with given genus
Sarah Loeb, Thomas Mahoney, Benjamin Reiniger, Jennifer Wise
Discret. Appl. Math.2
2017 Large-Scale Occupational Skills Normalization for Online Recruitment
Faizan Javed, Phuong Hoang, Thomas Mahoney, Matt McNair
AAAI3