Don’t Take It Personally: Resistance to Individually Targeted Recommendations from Conversational Recommender Agents

Laban, G. and Araujo, T. (2022) Don’t Take It Personally: Resistance to Individually Targeted Recommendations from Conversational Recommender Agents. In: 10th International Conference on Human-Agent Interaction (HAI '22), Christchurch, New Zealand, 05-08 Dec 2022, pp. 57-66. ISBN 9781450393232 (doi: 10.1145/3527188.3561929)

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Abstract

Conversational recommender agents are artificially intelligent recommender systems that provide users with individually-tailored recommendations by targeting individual needs and communicating in a flowing dialogue. These are widely available online, communicating with users while demonstrating human-like (anthropomorphic) social cues. Nevertheless, little is known about the effect of their anthropomorphic cues on users’ resistance to the system and recommendations. Accordingly, this study examined the extent to which conversational recommender agents’ anthropomorphic cues and the type of recommendations provided (user-initiated and system-initiated) influenced users’ perceptions of control, trustworthiness, and the risk of using the platform. The study assessed how these perceptions, in turn, influence users’ adherence to the recommendations. An online experiment was conducted among users with conversational recommender agents and web recommender platforms that provided user-initiated or system-initiated restaurant recommendations. The results entail that user-initiated recommendations, compared to system-initiated, are less likely to affect users’ resistance to the system and are more likely to affect their adherence to the recommendations provided. Furthermore, the study’s findings suggest that these effects are amplified for conversational recommender agents, demonstrating anthropomorphic cues, in contrast to traditional systems as web recommender platforms.

Item Type:Conference Proceedings
Keywords:E-commerce, chatbots, anthropomorphism, conversational agents, recommender systems, privacy, trust, personalization.
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
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Research Group:Social Brain in Action
Publisher:Association for Computing Machinery
ISBN:9781450393232
Copyright Holders:Copyright © 2022 Association for Computing Machinery
First Published:First published in Proceedings of the 10th International Conference on Human-Agent Interaction: 57-66
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
306871The European Training Network on Informal CareEmily CrossEuropean Commission (EC)814072Centre for Neuroscience