Evaluating systematic transactional data enrichment and reuse

Jim Hahn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish (US)
Title of host publicationProceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse, AIDR 2019
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450371841
DOIs
StatePublished - May 13 2019
Event2019 Conference on Artificial Intelligence for Data Discovery and Reuse, AIDR 2019 - Pittsburgh, United States
Duration: May 13 2019May 15 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2019 Conference on Artificial Intelligence for Data Discovery and Reuse, AIDR 2019
CountryUnited States
CityPittsburgh
Period5/13/195/15/19

Keywords

  • Data reuse
  • Machine learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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