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Existing approaches to recipe generation are unable to create recipes forusers with culinary preferences but incomplete knowledge of ingredients inspecific dishes. We propose a new task of personalized recipe generation tohelp these users: expanding a name and incomplete ingredient details intocomplete natural-text instructions aligned with the user's historicalpreferences. We attend on technique- and recipe-level representations of auser's previously consumed recipes, fusing these 'user-aware' representationsin an attention fusion layer to control recipe text generation. Experiments ona new dataset of 180K recipes and 700K interactions show our model's ability togenerate plausible and personalized recipes compared to non-personalizedbaselines.

Generating Personalized Recipes from Historical User Preferences