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The Effect of Personalization Techniques in Users’ Perceptions of Conversational Recommender Systems

Laban, G. and Araujo, T. (2020) The Effect of Personalization Techniques in Users’ Perceptions of Conversational Recommender Systems. In: 20th ACM International Conference on Intelligent Virtual Agents (IVA ’20), Scotland, Virtual Event, UK, 20-22 Oct 2020, p. 34. ISBN 9781450375863 (doi: 10.1145/3383652.3423890)

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Abstract

Conversational recommender systems provide users with individually tailored recommendations in a flowing dialogue. These require users to disclose information proactively or reactively for receiving personalized recommendations, which can trigger users' resistance to the platform and to the recommendations. Accordingly, this study examined the extent to which user-initiated and system-initiated recommendations provided by a conversational recommender system influenced users' perceptions of it. The results of an online experiment entail that when recommendations are system-initiated, as compared to user-initiated, users perceive to be in less control and perceive the system as riskier. Furthermore, the results stress that systems that provide user-initiated or system-initiated recommendations do not differ in users' perceptions of anthropomorphism.

Item Type:	Conference Proceedings
Keywords:	Recommender systems, conversational agents, personalization, chatbots, E-commerce, anthropomorphism, privacy.
Status:	Published
Refereed:	Yes
Glasgow Author(s) Enlighten ID:	Laban, Mr Guy
Authors:	Laban, G., and Araujo, T.
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General)
College/School:	College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Research Group:	Social Brain in Action Lab
ISBN:	9781450375863
Copyright Holders:	Copyright © 2020 The Author
First Published:	First published in IVA '20: Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents: 34
Publisher Policy:	Reproduced in accordance with the publisher copyright policy

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References

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Deposit and Record Details

ID Code:	225702
Depositing User:	Mr Guy Laban
Datestamp:	26 Oct 2020 10:51
Last Modified:	26 Oct 2020 10:51
Date of acceptance:	8 September 2020
Date of first online publication:	20 October 2020
Date Deposited:	24 October 2020
Data Availability Statement:	No