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Personalising explainable recommendations: Literature and conceptualisation

Personalising explainable recommendations: Literature and conceptualisation
Personalising explainable recommendations: Literature and conceptualisation

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, Human-computer interaction, Intelligent systems, Personalisation
2194-5357
518-533
Springer
Naiseh, Mohammad
ab9d6b3c-569c-4d7c-9bfd-61bbb8983049
Jiang, Nan
ef83d2d6-09b2-4d7a-845a-8ffbe8a67dd3
Ma, Jianbing
b7f6768c-a2d7-4e5c-8b2b-f6af2bba624f
Ali, Raian
a8042ed0-9c68-49b2-885f-bc9932eb65b0
Rocha, Álvaro
Adeli, Hojjat
Reis, Luís Paulo
Costanzo, Sandra
Orovic, Irena
Moreira, Fernando
Naiseh, Mohammad
ab9d6b3c-569c-4d7c-9bfd-61bbb8983049
Jiang, Nan
ef83d2d6-09b2-4d7a-845a-8ffbe8a67dd3
Ma, Jianbing
b7f6768c-a2d7-4e5c-8b2b-f6af2bba624f
Ali, Raian
a8042ed0-9c68-49b2-885f-bc9932eb65b0
Rocha, Álvaro
Adeli, Hojjat
Reis, Luís Paulo
Costanzo, Sandra
Orovic, Irena
Moreira, Fernando

Naiseh, Mohammad, Jiang, Nan, Ma, Jianbing and Ali, Raian (2020) Personalising explainable recommendations: Literature and conceptualisation. Rocha, Álvaro, Adeli, Hojjat, Reis, Luís Paulo, Costanzo, Sandra, Orovic, Irena and Moreira, Fernando (eds.) In Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. vol. 1160, Springer. pp. 518-533 . (doi:10.1007/978-3-030-45691-7_49).

Record type: Conference or Workshop Item (Paper)

Abstract

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.

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Personalising_Explainable_Recommendation - Accepted Manuscript
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More information

Published date: 2020
Venue - Dates: 8th World Conference on Information Systems and Technologies, WorldCIST 2020, , Budva, Montenegro, 2020-04-06 - 2020-04-09
Keywords: Explanations, Human-computer interaction, Intelligent systems, Personalisation

Identifiers

Local EPrints ID: 455674
URI: http://eprints.soton.ac.uk/id/eprint/455674
ISSN: 2194-5357
PURE UUID: 8c1f1862-e819-4f6e-bffb-8a1136150a78
ORCID for Mohammad Naiseh: ORCID iD orcid.org/0000-0002-4927-5086

Catalogue record

Date deposited: 30 Mar 2022 16:44
Last modified: 28 Apr 2022 02:32

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Contributors

Author: Mohammad Naiseh ORCID iD
Author: Nan Jiang
Author: Jianbing Ma
Author: Raian Ali
Editor: Álvaro Rocha
Editor: Hojjat Adeli
Editor: Luís Paulo Reis
Editor: Sandra Costanzo
Editor: Irena Orovic
Editor: Fernando Moreira

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