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Account-based recommenders in open discovery environments

Account-based recommenders in open discovery environments This paper aims to introduce a machine learning-based “My Account” recommender for implementation in open discovery environments such as VuFind among others.Design/methodology/approachThe approach to implementing machine learning-based personalized recommenders is undertaken as applied research leveraging data streams of transactional checkout data from discovery systems.FindingsThe authors discuss the need for large data sets from which to build an algorithm and introduce a prototype recommender service, describing the prototype’s data flow pipeline and machine learning processes.Practical implicationsThe browse paradigm of discovery has neglected to leverage discovery system data to inform the development of personalized recommendations; with this paper, the authors show novel approaches to providing enhanced browse functionality by way of a user account.Originality/valueIn the age of big data and machine learning, advances in deep learning technology and data stream processing make it possible to leverage discovery system data to inform the development of personalized recommendations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Digital Library Perspectives Emerald Publishing

Account-based recommenders in open discovery environments

Digital Library Perspectives , Volume 34 (1): 7 – Jan 3, 2018

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Publisher
Emerald Publishing
Copyright
© Jim Hahn & Courtney McDonald.
ISSN
2059-5816
DOI
10.1108/dlp-07-2017-0022
Publisher site
See Article on Publisher Site

Abstract

This paper aims to introduce a machine learning-based “My Account” recommender for implementation in open discovery environments such as VuFind among others.Design/methodology/approachThe approach to implementing machine learning-based personalized recommenders is undertaken as applied research leveraging data streams of transactional checkout data from discovery systems.FindingsThe authors discuss the need for large data sets from which to build an algorithm and introduce a prototype recommender service, describing the prototype’s data flow pipeline and machine learning processes.Practical implicationsThe browse paradigm of discovery has neglected to leverage discovery system data to inform the development of personalized recommendations; with this paper, the authors show novel approaches to providing enhanced browse functionality by way of a user account.Originality/valueIn the age of big data and machine learning, advances in deep learning technology and data stream processing make it possible to leverage discovery system data to inform the development of personalized recommendations.

Journal

Digital Library PerspectivesEmerald Publishing

Published: Jan 3, 2018

Keywords: Discovery; Personalization; Recommendations; Machine learning; Open algorithm; Research libraries

References