Hagemann, NinaNinaHagemannO'Mahony, Michael P.Michael P.O'MahonySmyth, BarryBarrySmyth2019-05-072019-05-072018 the A2018-10-02978-1-4503-5901-6http://hdl.handle.net/10197/10291The 12th ACM Conference on Recommender Systems (RecSys '18), Vancouver, Canada, 2 October 2018Recommender 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.enThis is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in RecSys '18 Proceedings of the 12th ACM Conference on Recommender Systems http://doi.acm.org/10.1145/3240323.3241613Recommender systemsContent-based filteringDiversityConference Publication10.1145/3240323.32416132018-09-03https://creativecommons.org/licenses/by-nc-nd/3.0/ie/