Personalising Explainable Recommendations: Literature and Conceptualisation

  • Mohammad NaisehEmail author
  • Nan Jiang
  • Jianbing Ma
  • Raian Ali
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1160)


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.


Explanations Personalisation Human-computer interaction Intelligent systems 



This work is partially funded by iQ HealthTech and Bournemouth university PGR development fund.


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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mohammad Naiseh
    • 1
    Email author
  • Nan Jiang
    • 1
  • Jianbing Ma
    • 2
  • Raian Ali
    • 3
  1. 1.Faculty of Science and TechnologyBournemouth UniversityPooleUK
  2. 2.Chengdu University of Information TechnologyChengduChina
  3. 3.Division of Information and Computing Technology, College of Science and EngineeringHamad Bin Khalifa UniversityDohaQatar

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