Prediction of Food Ingredient Pairings using Siamese Neural Networks

The study introduces a novel method for predicting food ingredient pairings using siamese neural networks, a type of neural network architecture renowned for its capacity to learn embeddings and capture semantic similarity. The accurate prediction of ingredient pairings holds significance for recipe recommendation systems and culinary innovation. The proposed approach entails training siamese neural networks on a substantial dataset of recipes, where the networks acquire embeddings for each ingredient and assess their compatibility through comparison. The effectiveness of the method is demonstrated through comprehensive evaluation on the dataset, highlighting its capability to accurately forecast ingredient pairings.