@inproceedings{4a977884a2ca48c6b219c21ea059c81c,
title = "Evaluating systematic transactional data enrichment and reuse",
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 [13] 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.",
keywords = "Data reuse, Machine learning",
author = "Jim Hahn",
note = "Publisher Copyright: {\textcopyright} 2019 held by the owner/author(s). Publication rights licensed to ACM.; 2019 Conference on Artificial Intelligence for Data Discovery and Reuse, AIDR 2019 ; Conference date: 13-05-2019 Through 15-05-2019",
year = "2019",
month = may,
day = "13",
doi = "10.1145/3359115.3359116",
language = "English (US)",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the Conference on Artificial Intelligence for Data Discovery and Reuse, AIDR 2019",
address = "United States",
}