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Title:Evaluating systematic transactional data enrichment and reuse
Author(s):Hahn, James F.
Subject(s):data reuse
machine learning
data mining
association rules
personalization
network science
Recommender systems
Digital libraries
Information systems
Abstract:A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 check-outs sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this paper is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.
Issue Date:2019-05-13
Publisher:ACM
Citation Info:Jim Hahn. 2019. Evaluating systematic transactional data enrichment and reuse. In Artificial Intelligence for Data Discovery and Reuse 2019 (AIDR '19), May 13–15, 2019, Pittsburgh, PA, USA. ACM, New York, NY, USA. https://doi.org/10.1145/3359115.3359116
Genre:Conference Paper / Presentation
Type:Text
Image
Language:English
URI:http://hdl.handle.net/2142/105401
ISBN:978-1-4503-7184-1/19/05
DOI:https://doi.org/10.1145/3359115.3359116
Sponsor:Research and Publication Committee of the University of Illinois at Urbana-Champaign Library
Rights Information:© Jim Hahn 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. Publication rights licensed to ACM. The definitive version was published in AIDR '19, May 13–15, 2019, Pittsburgh, PA, USA. ACM 978-1-4503-7184-1/19/05.
Date Available in IDEALS:2019-08-27


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