The Effect of Personalization Techniques in Users' Perceptions of Conversational Recommender Systems
Open accessAbstract
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.
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There is a growing base of research to inform design of chatbot interactions, including conversational design, personalization of chatbots, the use of interactive elements in chatbots, and the use of social cues to indicate social status and capabilities.
“There is also a growing base of research to inform design of chatbot interactions, whether this concerns conversational design [6], personalization of chatbots [69], the use of interactive elements in chatbots [57], or the use of social cues to indicate social status and capabilities [32].”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [69]
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Future directions for chatbot research: An interdisciplinary research agenda
Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P., Ischen, C., et al. (2021) · Computing · doi:10.1007/s00607-021-01016-7 · Cited by 247 · see citing works
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- Authors
- Laban, G., & Araujo, T.
- Affiliations
- University of Glasgow (GB)
- University of Amsterdam (NL)
- Venue
- Proceedings of the 20th ACM International Conference on Intelligent Virtual Agents
- Publisher
- ACM
- Published
- 2020-10-19
- Pages
- 1-3
- OpenAlex
- W3093761638
- Cited by
- 36
- References
- 22
- Citations by year
- 2025: 10 · 2024: 10 · 2023: 4 · 2022: 8 · 2021: 3 · 2020: 1
- FWCI
- 3.03
- Type
- conference paper
- Language
- en
- Open access
- green
- eprints.gla.ac.uk
- Locations
- ACM International Conference on Intelligent Virtual Agents (IVA) (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion, cc-by-sa)
- UvA-DARE (University of Amsterdam) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- conversational recommender systems, personalization, user control, risk perception, anthropomorphism, user perception
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