Module Advisor: Guiding Students with Recommendations
|Title:||Module Advisor: Guiding Students with Recommendations||Authors:||Hagemann, Nina
O'Mahony, Michael P.
|Permanent link:||http://hdl.handle.net/10197/10164||Date:||17-May-2018||Online since:||2019-04-25T11:46:18Z||Abstract:||Personalised recommendations feature prominently in many aspects of our lives, from the movies we watch, to the news we read, and even the people we date. However, one area that is still relatively underdeveloped is the educational sector where recommender systems have the potential to help students to make informed choices about their learning pathways. We aim to improve the way students discover elective modules by using a hybrid recommender system that is specifically designed to help students to better explore available options. By combining notions of content-based similarity and diversity, based on structural information about the space of modules, we can improve the discoverability of long-tail options that may uniquely suit students preferences and aspirations.||Type of material:||Conference Publication||Publisher:||Springer||Start page:||319||End page:||325||Series/Report no.:||Lecture Notes in Computer Science||Copyright (published version):||2018 Springer||Keywords:||Recommender systems; Content-based filtering; Diversity; Collaborative filtering; Module recommendations; Elective modules||DOI:||10.1007/978-3-319-91464-0_34||Other versions:||http://its2018.its-conferences.com/||Language:||en||Status of Item:||Peer reviewed||Is part of:||Lecture Notes in Computer Science (LNCS, volume 10858)||Conference Details:||The 14th International Conference (ITS 2018), Montreal, Canada, 11-15 June 2018|
|Appears in Collections:||Insight Research Collection|
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