Evaluating systematic transactional data enrichment and reuse

Hahn, James F.

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https://hdl.handle.net/2142/105401 Copy

Description

Title

Evaluating systematic transactional data enrichment and reuse

Author(s)

Hahn, James F.

Issue Date

2019-05-13

Keyword(s)

• data reuse
• machine learning
• data mining
• association rules
• personalization
• network science
• Recommender systems
• Digital libraries
• Information systems

Date of Ingest

2019-08-27T16:51:55Z

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.

Publisher

ACM

Type of Resource

• text
• image

Genre of Resource

Conference Paper / Presentation

Language

en

Permalink

http://hdl.handle.net/2142/105401

DOI

https://doi.org/10.1145/3359115.3359116

Sponsor(s)/Grant Number(s)

Research and Publication Committee of the University of Illinois at Urbana-Champaign Library

Copyright and License 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.

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