Hagemann, NinaNinaHagemannO'Mahony, Michael P.Michael P.O'MahonySmyth, BarryBarrySmyth2019-04-252019-04-252018 Sprin2018-05-17http://hdl.handle.net/10197/10164The 14th International Conference (ITS 2018), Montreal, Canada, 11-15 June 2018Personalised 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.enThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-91464-0_34.Recommender systemsContent-based filteringDiversityCollaborative filteringModule recommendationsElective modulesConference Publication31932510.1007/978-3-319-91464-0_342018-08-23https://creativecommons.org/licenses/by-nc-nd/3.0/ie/