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Personalising explainable recommendations: Literature and conceptualisation.

Naiseh, M., Jiang, N., Ma, J. and Ali, R., 2020. Personalising explainable recommendations: Literature and conceptualisation. In: Trends and Innovations in Information Systems and Technologies: WorldCIST 2020 Proceedings, 7-10 April 2020, Budva, Montenegro, 518 - 533.

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Personalising_Explainable_Recommendation.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial.


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DOI: 10.1007/978-3-030-45691-7_49


Explanations in intelligent systems aim to enhance a users’ understandability of their reasoning process and the resulted decisions and recommendations. Explanations typically increase trust, user acceptance and retention. The need for explanations is on the rise due to the increasing public concerns about AI and the emergence of new laws, such as the General Data Protection Regulation (GDPR) in Europe. However, users are different in their needs for explanations, and such needs can depend on their dynamic context. Explanations suffer the risk of being seen as information overload, and this makes personalisation more needed. In this paper, we review literature around personalising explanations in intelligent systems. We synthesise a conceptualisation that puts together various aspects being considered important for the personalisation needs and implementation. Moreover, we identify several challenges which would need more research, including the frequency of explanation and their evolution in tandem with the ongoing user experience.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Explanations; Personalisation; Human-Computer Interaction; Intelligent Systems
Group:Faculty of Science & Technology
ID Code:34805
Deposited By: Symplectic RT2
Deposited On:12 Nov 2020 15:23
Last Modified:14 Mar 2022 14:25


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