Abstract
Predicting food preferences is challenging due to the numerous factors that can influence individual taste. Cultural influences are one such factor that can significantly impact food preferences. Irrespective of culture, however, food visual aesthetics drive food choice. With this in mind, we study 15,000 images of food from prominent recipe portals in China, the US and Germany with the aim of ...
Abstract
Predicting food preferences is challenging due to the numerous factors that can influence individual taste. Cultural influences are one such factor that can significantly impact food preferences. Irrespective of culture, however, food visual aesthetics drive food choice. With this in mind, we study 15,000 images of food from prominent recipe portals in China, the US and Germany with the aim of identifying common visual trends related to food choice. We report that distinguishing between appreciated and less appreciated recipes based on visual features through multiple machine learning experiments is possible, with a maximum accuracy of 67%. The classifiers trained on one culture, when applied to other cultures, reveal that appreciated recipes from US and German portals share visual similarities, while the visual aspects making recipes attractive for Chinese users differ. Complementing our machine learning experiments, we conducted a user study with 450 participants from the three cultures. Participants are asked to rank recipes based on their appeal and provide justification through answering 19 questions. The results reveal significant predictors of preference, including perceived appearance and taste across all countries, and negative perception of perceived healthiness for US and German participants. Analysis of 77 consistently labelled appealing/non-appealing images reveal significant variations in low-level visual features such as colourfulness, sharpness, and contrast. These findings suggest that these low-level visual features may play a critical role in determining whether an image is perceived as appealing or not across different cultures. Our results offer promising insights into the development of cross-culture food recommender systems. By demonstrating common ground across food cultures, we believe that these systems can provide personalised meal suggestions that reflect user's preferences while incorporating ideals such as healthfulness and sustainability.
Metadata last modified: 18 Mar 2025 10:05
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