The Case for Graph-Based Recommendations

Popescu, I., Portelli, K., Anagnostopoulos, C. and Ntarmos, N. (2018) The Case for Graph-Based Recommendations. In: 2017 IEEE International Conference on Big Data (IEEE BigData 2017), Boston, MA, USA, 11-14 Dec 2017, pp. 4819-4821. ISBN 9781538627150 (doi: 10.1109/BigData.2017.8258553)

151454.pdf - Accepted Version



Recommender systems have been intensively used to create personalised profiles, which enhance the user experience. In certain areas, such as e-learning, this approach is short-sighted, since each student masters each concept through different means. The progress from one concept to the next, or from one lesson to another, does not necessarily follow a fixed pattern. Given these settings, we can no longer use simple structures (vectors, strings, etc.) to represent each user's interactions with the system, because the sequence of events and their mapping to user's intentions, build up into more complex synergies. As a consequence, we propose a graph-based interpretation of the problem and identify the challenges behind (a) using graphs to model the users' journeys and hence as the input to the recommender system, and (b) producing recommendations in the form of graphs of actions to be taken.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Portelli, Mr Kurt and Anagnostopoulos, Dr Christos and Ntarmos, Dr Nikos and Paun, Ms Iulia
Authors: Popescu, I., Portelli, K., Anagnostopoulos, C., and Ntarmos, N.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2018 IEEE
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
736751PRIMES: Personalised Recommendations and Internationalisation for MOOCs in European SchoolsNikolaos NtarmosEuropean Commission (EC)KA201-024631SCHOOL OF COMPUTING SCIENCE