Module Advisor: A Hybrid Recommender System for Elective Module Exploration

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Title: Module Advisor: A Hybrid Recommender System for Elective Module Exploration
Authors: Hagemann, Nina
O'Mahony, Michael P.
Smyth, Barry
Permanent link: http://hdl.handle.net/10197/10291
Date: 2-Oct-2018
Online since: 2019-05-07T07:37:35Z
Abstract: Recommender systems are omni-present in our every day lives, guiding us through the vast amount of information available. However, in the academic world, personalised recommendations are less prominent, leaving students to navigate through the typically large space of available courses and modules manually. Since it is crucial for students to make informed choices about their learning pathways, we aim to improve the way students discover elective modules by developing a hybrid recommender system prototype that is specifically designed to help students find elective modules from a diverse set of subjects. We can improve the discoverability of long-tail options and help students broaden their horizons by combining notions of similarity and diversity.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2018 the Authors
Keywords: Recommender systemsContent-based filteringDiversity
DOI: 10.1145/3240323.3241613
Other versions: https://recsys.acm.org/recsys18/
Language: en
Status of Item: Peer reviewed
Is part of: RecSys '18 Proceedings of the 12th ACM Conference on Recommender Systems
Conference Details: The 12th ACM Conference on Recommender Systems (RecSys '18), Vancouver, Canada, 2 October 2018
ISBN: 978-1-4503-5901-6
Appears in Collections:Insight Research Collection

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