P
Panagiotis G. Ipeirotis
Researcher at New York University
Publications - 120
Citations - 23251
Panagiotis G. Ipeirotis is an academic researcher from New York University. The author has contributed to research in topics: Ranking & Product (category theory). The author has an hindex of 46, co-authored 118 publications receiving 21363 citations. Previous affiliations of Panagiotis G. Ipeirotis include University of Amsterdam & Columbia University.
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Running experiments on Amazon Mechanical Turk
TL;DR: The authors presented new demographic data about the Mechanical Turk subject population, reviewed the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and compared the magnitude of effects obtained using Mechanical Turk and traditional subject pools.
Posted Content
Running experiments on Amazon Mechanical Turk
TL;DR: The authors presented new demographic data about the Mechanical Turk subject population, reviewed the strengths of Mechanical Turk relative to other online and offline methods of recruiting subjects, and compared the magnitude of effects obtained using Mechanical Turk and traditional subject pools.
Journal ArticleDOI
Duplicate Record Detection: A Survey
TL;DR: This paper presents an extensive set of duplicate detection algorithms that can detect approximately duplicate records in a database and covers similarity metrics that are commonly used to detect similar field entries.
Proceedings ArticleDOI
Get another label? improving data quality and data mining using multiple, noisy labelers
TL;DR: The results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.
Posted Content
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics
TL;DR: This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.