Model for Cooking Recipe Generation using Reinforcement Learning
Jumpei Fujita, Masahiro Sato, Hajime Nobuhara · 2021
It is difficult to find a recipe that uses the ingredients in a person's refrigerator within a short time. To solve this problem, we propose a recipe-generation model in the encoder- decoder framework. Models developed in the traditional encoder- decoder framework do not adequately reflect the ingredients in cooking recipes, but the proposed method introduces reinforcement learning and coverage loss. The model was experimentally evaluated on a dataset of approximately 15 K cooking recipes extracted from Food.com. The evaluation index was ingredient matching (IM), a new evaluation metric, showing the extent to which the recipe uses the input ingredients. Relative to the existing model, the proposed model improved the IM by approximately 21%.