Generating Personalized Recipes from Historical User Preferences

We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete naturaltext instructions aligned with the user's historical preferences. We attend on technique- and recipe-level representations of a user's previously consumed recipes, fusing these 'useraware' representations in an attention fusion layer to control recipe text generation.

Experiments on a new dataset of 180K recipes and 700K interactions show our model's ability to generate plausible and personalized recipes compared to non-personalized baselines.