Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies

Florian Pecune*, Lucile Callebert, Stacy Marsella

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number733835
JournalFrontiers in Robotics and AI
Volume8
DOIs
Publication statusPublished - 2022 Jan 19
Externally publishedYes

Keywords

  • conversational agents
  • food recommender systems
  • health aware
  • nudging
  • socially aware

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence

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