Visualising Module Dependencies in Academic Recommendations

Files in This Item:
File Description SizeFormat 
insight_publication.pdf2.29 MBAdobe PDFDownload
Title: Visualising Module Dependencies in Academic Recommendations
Authors: Hagemann, NinaO'Mahony, Michael P.Smyth, Barry
Permanent link: http://hdl.handle.net/10197/10796
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
Other versions: https://iui.acm.org/2019/
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

Show full item record

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.