Kazjon Grace
University of Sydney, Sydney, Australia
Choosing what to eat requires navigating a large volume of information and various competing factors. While recommendation systems are an effective approach to assist users with this culinary decision-making, they typically prioritise similarity to a query or user profile to give relevant results. This can expose users to an increasingly narrow band of phenomena, which could compromise dietary diversity, a factor in dietary quality. We designed Q-Chef, which combines a recipe recommendation system with a personalised model of surprise, and conducted a study to identify if surprise-eliciting recipes affect food decisions. Our study utilises a rigorous thematic analysis with over 40 participants to explore how computational models of surprise influence recipe choice. We also explored how these factors differed when people were presented with "surprising-yet-tasty" recipes, as opposed to just "tasty" recipes, and identified that being presented with surprising choices is more likely to elicit situational interest and prompt reflection on choices. We conclude with a set of suggestions for the design of future surprise-eliciting recipe systems.
https://dl.acm.org/doi/abs/10.1145/3491102.3501862
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)