Designing persuasive food conversational recommender systems with nudging and socially-aware conversational strategies

Pecune, F., Callebert, L. and Marsella, S. (2022) Designing persuasive food conversational recommender systems with nudging and socially-aware conversational strategies. Frontiers in Robotics and AI, 8, 733835. (doi: 10.3389/frobt.2021.733835) (PMID:35127834) (PMCID:PMC8807554)

Abstract

Unhealthy eating behavior is a major public health issue with serious repercussions on an individual’s health. One potential solution to overcome this problem, and help people change their eating behavior, is to develop conversational systems able to recommend healthy recipes. One challenge for such systems is to deliver personalized recommendations matching users’ needs and preferences. Beyond the intrinsic quality of the recommendation itself, various factors might also influence users’ perception of a recommendation. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users’ eating habits and current preferences. Users can interact with Cora in two different ways. They can select pre-defined answers by clicking on buttons to talk to Cora or write text in natural language. Additionally, Cora can engage users through a social dialogue, or go straight to the point. Cora is also able to propose different alternatives and to justify its recipes recommendation by explaining the trade-off between them. We conduct two experiments. In the first one, we evaluate the impact of Cora’s conversational skills and users’ interaction mode on users’ perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users’ perception of the interaction as well as their perception of the system. In the second evaluation, we evaluate the influence of Cora’s explanations and recommendation comparisons on users’ perception. Our results show that explanations positively influence users’ perception of a recommender system. However, comparing healthy recipes with a decoy is a double-edged sword. Although such comparison is perceived as significantly more useful compared to one single healthy recommendation, explaining the difference between the decoy and the healthy recipe would actually make people less likely to use the system.

• Item Type: Articles
• Keywords: Robotics and AI, health aware, food recommender systems, conversational agents, nudging, socially aware.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Callebert, Ms Lucile and Marsella, Professor Stacy and Pecune, Mr Florian
• Authors: Pecune, F., Callebert, L., and Marsella, S.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Frontiers in Robotics and AI
• Publisher: Frontiers Media
• ISSN: 2296-9144
• ISSN (Online): 2296-9144
• Copyright Holders: Copyright © 2022 Pecune, Callebert and Marsella
• First Published: First published in Frontiers in Robotics and AI 8: 733835
• Publisher Policy: Reproduced under a Creative Commons License