Module Advisor: A Hybrid Recommender System for Elective Module Exploration
|Title:||Module Advisor: A Hybrid Recommender System for Elective Module Exploration||Authors:||Hagemann, Nina
O'Mahony, Michael P.
|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 systems; Content-based filtering; Diversity||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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