Visualising Module Dependencies in Academic Recommendations

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Title: Visualising Module Dependencies in Academic Recommendations
Authors: Hagemann, NinaO'Mahony, Michael P.Smyth, Barry
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Date: 20-Mar-2019
Online since: 2019-06-12T11:44:26Z
Abstract: Starting their academic career can be overwhelming for many young people. Students are often presented with a variety of options within their programmes of study and making appropriate and informed decisions can be a challenge. Compared to many other areas in our every day life, recommender systems remain under used in the academic setting. In this part of our research we use non-negative matrix factorisation to identify dependencies between modules, visualise sequential recommendations, and bring structure and clarity into the academic module space.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Start page: 77
End page: 78
Copyright (published version): 2019 Association for Computing Machinery
Keywords: Recommender systemsNon-negative matrix factorisationAcademic advising
DOI: 10.1145/3308557.3308701
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Language: en
Status of Item: Peer reviewed
Is part of: IUI '19 Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion
Conference Details: ACM IUI 2019: 24th International Conference on Intelligent User Interfaces, Los Angeles, USA, 16-20 March 2019
ISBN: 978-1-4503-6673-1
Appears in Collections:Insight Research Collection

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