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Title:Account-based recommenders in open discovery environments
Author(s):James F. Hahn; Courtney Mcdonald
Subject(s):Discovery
recommendations
open algorithm
machine learning
research libraries
personalization
Abstract:This paper aims to introduce a machine learning-based “My Account” recommender for implementation in open discovery environments such as VuFind among others. The approach to implementing machine learning-based personalized recommenders is undertaken as applied research leveraging data streams of transactional checkout data from discovery systems. The 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. The 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. In 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.
Issue Date:2018
Publisher:Emerald Publishing Limited
Citation Info:Hahn, J., & Mcdonald, C. (2018). Account-based recommenders in open discovery environments. Digital Library Perspectives, 34(1). DOI: 10.1108/DLP-07-2017-0022
Genre:Article
Type:Text
Language:English
URI:http://hdl.handle.net/2142/98916
DOI:10.1108/DLP-07-2017-0022
Sponsor:University of Illinois Campus Research Board (RB16001)
Rights Information:© Jim Hahn & Courtney McDonald. 2017
Date Available in IDEALS:2017-12-18


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