Exploring Contextual Paradigms in Context-Aware Recommendations

Morgan, C., Paun, I. and Ntarmos, N. (2020) Exploring Contextual Paradigms in Context-Aware Recommendations. In: 4th IEEE Workshop on Human-in-the-Loop Methods and Future of Work in Big Data 2020, IEEE Big Data 2020, Atlanta, GA, USA, 10-13 Dec 2020, ISBN 9781728162515 (doi:10.1109/BigData50022.2020.9377964)

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Abstract

Traditional recommendation systems utilise past users’ preferences to predict unknown ratings and recommend unseen items. However, as the number of choices from content providers increases, additional information, such as context, has to be included in the recommendation process to improve users’ satisfaction. Context-aware recommendation systems exploit the users’ contextual information (e.g., location, mood, company, etc.) using three main paradigms: contextual pre-filtering, contextual post-filtering, and contextual modelling. In this work, we explore these three ways of incorporating context in the recommendation pipeline, and compare them on context-aware datasets with different characteristics. The experimental evaluation showed that contextual pre-filtering and contextual modelling yield similar performance, while the post-filtering approach achieved poorer accuracy, emphasising the importance of context in producing good recommendations.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ntarmos, Dr Nikos and Paun, Ms Iulia
Authors: Morgan, C., Paun, I., and Ntarmos, N.
College/School:College of Science and Engineering > School of Computing Science
ISBN:9781728162515
Published Online:19 March 2021
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in 2020 IEEE International Conference on Big Data (Big Data)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services