Papers › Generating Personalized Recipes from Historical User Preferences
Generating Personalized Recipes from Historical User Preferences
Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley
Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. We propose a new task of personalized recipe generation to help these users: expanding a name and incomplete ingredient details into complete natural-text 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 'user-aware' 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.
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Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recipe Generation | Food.com | Prior Name | BLEU-1 | 28.046 | #2 of 2 | Archive leaderboard | report |
| Recipe Generation | Food.com | Prior Name | BLEU-4 | 3.211 | #2 of 2 | Archive leaderboard | report |
| Recipe Generation | Food.com | Prior Name | BPE Perplexity | 9.516 | #2 of 2 | Archive leaderboard | report |
| Recipe Generation | Food.com | Prior Name | D-1 | 0.233 | #2 of 2 | Archive leaderboard | report |
| Recipe Generation | Food.com | Prior Name | D-2 | 2.08 | #2 of 2 | Archive leaderboard | report |
| Recipe Generation | Food.com | Prior Name | Rouge-L | 24.794 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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