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000000065 02470 $$2doi$$a10.1108/DLP-07-2017-0022
000000065 037__ $$aPUBART
000000065 041__ $$aeng
000000065 245__ $$aAccount-based recommenders in open discovery environments
000000065 269__ $$a2018
000000065 300__ $$a70-76
000000065 336__ $$aPublished Article
000000065 500__ $$aThis article was first published as follows:  Jim Hahn and Courtney McDonald, (2018) "Account-based recommenders in open discovery environments", Digital Library Perspectives, Vol. 34 Issue: 1, pp.70-76, https://doi.org/10.1108/DLP-07-2017-0022
000000065 520__ $$aThis 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.
000000065 542__ $$fCC BY-NC-SA
000000065 6531_ $$aDiscovery
000000065 6531_ $$aPersonalization
000000065 6531_ $$aRecommendations
000000065 6531_ $$aMachine learning
000000065 6531_ $$aOpen algorithm
000000065 6531_ $$aResearch libraries
000000065 700__ $$aHahn, Jim
000000065 700__ $$aMcDonald, Courtney
000000065 773__ $$j34$$k1$$tDigital Library Perspectives
000000065 8560_ $$fcrgreene@indiana.edu
000000065 8564_ $$s138230$$uhttps://iu.tind.io/record/65/files/DLP-07-2017-0022.pdf
000000065 8564_ $$s1648071$$uhttps://iu.tind.io/record/65/files/DLP-07-2017-0022.pdf?subformat=pdfa$$xpdfa
000000065 85642 $$hElectronic Resource
000000065 960__ $$aAssociate faculty
000000065 980__ $$aPUBART