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Ben Russell

dblp:68/6910 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2016
—ORCID · conflict

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

Artificial intelligence and machine learning · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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.

Artificial intelligence
1 paper
Learning theory · 50% Transfer learning and domain adaptation · 50%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory › hypothesis testing
a/b testing		 0.212016
Measuring the behavioral impact of machine translation quality improvements with A/B testing · EMNLP 2016
Machine learning › Transfer learning and domain adaptation
domain adaptation		 0.212016
Measuring the behavioral impact of machine translation quality improvements with A/B testing · EMNLP 2016
Computational social science and digital humanities › social computing
user behavior analysis		 0.112016
Measuring the behavioral impact of machine translation quality improvements with A/B testing · EMNLP 2016

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

crowdsourced evaluation · 0.5a/b testing · 0.5
YearPublicationVenuePosition
2016 Measuring the behavioral impact of machine translation quality improvements with A/B testing
abstract
In this paper we discuss a process for quantifying the behavioral impact of a domaincustomized machine translation system deployed on a large-scale e-commerce platform.We discuss several machine translation systems that we trained using aligned text from product listing descriptions written in multiple languages.We document the quality improvements of these systems as measured through automated quality measures and crowdsourced human quality assessments.We then measure the effect of these quality improvements on user behavior using an automated A/B testing framework.Through testing we observed an increase in key ecommerce metrics, including a significant increase in purchases.
Ben Russell, Duncan Gillespie
 EMNLP1
2002 Document Clustering Using the 1 + 1 Dimensional Self-Organising Map
Ben Russell, Hujun Yin, Nigel M. Allinson
 IDEAL1