biostudies-literatureUnknown4(10)Kojima HFood preferences differ among individuals, and these variations reflect underlying personalities or mental tendencies. However, capturing and predicting these individual differences remains challenging. Here, we propose a novel method to predict individual food preferences by using CLIP (Contrastive Language-Image Pre-Training), which can capture both visual and semantic features of food images. By applying this method to food image rating data obtained from human subjects, we demonstrated our method's prediction capability, which achieved better scores compared to methods using pixel-based embeddings or label text-based embeddings. Our method can also be used to characterize individual traits as characteristic vectors in the embedding space. By analyzing these individual trait vectors, wePLOS digital healthe0001044https://www.ebi.ac.uk/biostudies/studies/S-EPMC12561901biostudies-literaturePredicting individual food valuation via vision-language embedding model.PMC12561901Toyama ASuzuki SYamashita YKojima HfalsePredicting individual food valuation via vision-language embedding model.Food preferences differ among individuals, and these variations reflect underlying personalities or mental tendencies. However, capturing and predicting these individual differences remains challenging. Here, we propose a novel method to predict individual food preferences by using CLIP (Contrastive Language-Image Pre-Training), which can capture both visual and semantic features of food images. By applying this method to food image rating data obtained from human subjects, we demonstrated our method's prediction capability, which achieved better scores compared to methods using pixel-based embeddings or label text-based embeddings. Our method can also be used to characterize individual traits as characteristic vectors in the embedding space. By analyzing these individual trait vectors, we2025-01-01T00:00:00Z2025 Oct2026-06-05T09:11:44.961Z2026-05-15T03:11:56.913ZS-EPMC125619014115068510.1371/journal.pdig.0001044