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--- |
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dataset_info: |
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pretty_name: Recipe Cleaned Dataset |
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task_categories: |
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- recommendation |
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- text-classification |
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languages: |
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- en |
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size_categories: |
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- 100K<n<1M |
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license: mit |
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annotations_creators: |
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- machine |
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source_datasets: |
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- original |
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multilinguality: monolingual |
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--- |
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# Recipe Cleaned Dataset |
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## Dataset Summary |
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This dataset is a structured and cleaned collection of recipe data derived from the [Food.com Recipes and Interactions dataset](https://www.kaggle.com/datasets/shuyangli94/food-com-recipes-and-user-interactions/data?select=RAW_recipes.csv). It is designed for ingredient-based personalization, machine learning training, and interactive recommendation systems. The dataset integrates a hierarchical ingredient taxonomy, standardized nutrition information, and categorical metadata (e.g., diet tags, cuisine attributes, region) to support downstream filtering and modeling. |
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--- |
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## Source and Collection Process |
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The original data comes from Food.com via Kaggle. It consists of recipes and user interactions collected over several years, published under a permissive license for research and educational use. Only the `RAW_recipes.csv` portion was used in this dataset. No private or login-restricted data was collected. |
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--- |
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## Labeling and Cleaning Process |
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All labeling was performed using a **rule-based and embedding-based taxonomy mapping pipeline**. Ingredients were clustered into parent classes using sentence embeddings (~1,000+ parent nodes), and child phrases were mapped using keyword expansion and string matching. A small subset was manually inspected to ensure mapping accuracy. |
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Additional processing includes: |
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- Deduplication and invalid entry filtering |
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- Text normalization |
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- Region and diet tag standardization |
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- Nutrition field standardization |
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--- |
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## Ethical Notes |
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All data are derived from a publicly released Kaggle dataset. |
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No personal user information is included. |
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Nutrition values are estimated and must **not** be used for health or medical decisions. |
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--- |
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## Augmented/Synthetic Data |
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No augmented or synthetic samples are included in this dataset. |
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--- |
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## Dataset Structure |
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- **Format**: CSV (UTF-8) |
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- **Number of Recipes**: ~180,000 |
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- **Schema**: |
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- **Basic Info**: `name`, `minutes`, `tags`, `n_steps`, `steps` |
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- **Ingredients (raw & standardized)**: |
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- Raw ingredient lists split into categories: `staple`, `main`, `seasoning`, `other` |
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- Each category has a mapped parent version: e.g. `staple_parent`, `main_parent`, etc. |
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- `n_ingredients` gives the count of ingredients per recipe. |
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- **Nutrition**: `calories`, `total_fat_pdv`, `sugar_pdv`, `sodium_pdv`, `protein_pdv`, `saturated_fat_pdv`, `carbohydrates_pdv` |
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(all standardized per serving; PDV = % Daily Value) |
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- **Metadata**: `region` (51 classes), `diet_tags`, `cuisine_attr` (671 classes), `is_vegan_safe`, `is_vegetarian_safe` |
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--- |
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## Ingredient Taxonomy |
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To standardize heterogeneous ingredient strings, a parent–child taxonomy was constructed: |
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- Approximately 1,000+ parent ingredient classes generated through sentence embedding clustering. |
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- Original phrases (child nodes) are mapped via rule-based string matching and keyword expansion. |
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- Four functional categories are defined: |
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- **Staple**: e.g., rice, pasta, flour |
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- **Main**: protein sources or key components |
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- **Seasoning**: spices, condiments, sauces |
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- **Other**: toppings, garnishes, optional extras |
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This structure enables consistent representation across recipes, improves filtering granularity, and simplifies feature engineering for ML models. |
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--- |
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## Data Cleaning Pipeline |
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1. Deduplication & Filtering — remove exact/near-duplicates and invalid rows. |
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2. Text Normalization — lowercase conversion, punctuation cleanup, Unicode fixes. |
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3. Ingredient Categorization & Mapping — split into functional categories, map to parent classes. |
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4. Tag & Region Standardization — unify tags and infer cuisine/region attributes. |
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5. Nutrition Standardization — align units, compute PDV values, fill missing fields when possible. |
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--- |
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## Risks and Bias |
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The dataset inherits several biases from the original Food.com source: |
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- **Regional bias**: Western and American cuisines dominate, leading to under-representation of other cuisines. |
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- **Popularity bias**: Highly rated and frequently interacted recipes are over-represented, which may skew recommendation models. |
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- **Nutritional estimation bias**: Nutrition values are approximate and may vary from real-world measurements. |
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These biases may affect fairness and diversity in downstream models. |
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--- |
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## Intended Uses and Limitations |
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**Intended Uses** |
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- Ingredient-based recipe recommendation research |
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- Cold-start personalization and ranking model development |
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- Educational and academic applications |
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**Not Intended For** |
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- Medical or nutritional decision-making |
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- High-stakes personalization without bias mitigation |
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- Commercial deployment without further validation |
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--- |
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## Exploratory Data Analysis (EDA) |
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### Sample Entry |
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```json |
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{ |
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"name": "Spaghetti Aglio e Olio", |
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"minutes": 15, |
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"ingredients": ["spaghetti", "garlic", "olive oil", "red pepper flakes", "parsley", "salt"], |
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"main_parent": ["pasta"], |
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"seasoning_parent": ["garlic", "olive oil", "red pepper", "salt"], |
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"calories": 420, |
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"region": "Italian", |
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"diet_tags": ["vegetarian"], |
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"n_steps": 3 |
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} |
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