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--- |
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language: en |
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license: mit |
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tags: |
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- recommendation |
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- ranking |
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- personalization |
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- xgboost |
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- xgbranker |
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- recipe |
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- cold-start |
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datasets: |
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- your-username/recipe-cleaned-dataset |
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model-index: |
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- name: Personalized Recipe Ranking Models |
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results: |
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- task: |
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type: recommendation |
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name: Personalized Recipe Ranking |
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dataset: |
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name: Food.com (Cleaned) |
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type: your-username/recipe-cleaned-dataset |
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metrics: |
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- type: ndcg@5 |
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value: 0.44 |
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- type: ndcg@10 |
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value: 0.44 |
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--- |
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# Model Card: Personalized Recipe Ranking Models |
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## Overview |
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This project implements a personalized recipe recommendation system using two model categories: |
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1. **Scratch-trained baseline**: A simple rule-based + embedding matching ranker trained on a synthetic preference dataset (no user-specific rules). |
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2. **Rule-enhanced cold-start models**: Five separate XGBRanker models trained with more complex rule-based preference signals and user-specific interaction patterns (user1–user5). |
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The goal is to evaluate how different user profiles affect ranking behavior and recommendation diversity, even when overall NDCG scores are lower than the baseline. |
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--- |
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## Model Category 1: Scratch-trained Baseline |
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### Purpose |
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Provide a simple cold-start recommendation baseline that matches ingredients and ranks recipes without personalization. It uses parent–child ingredient overlap and a few numeric features (e.g., protein, cost, cooking time). |
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### Data Sources |
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- Cleaned Food.com dataset (~180k recipes) |
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- 10,000 synthetic preference samples generated via uniform random selection |
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### Training Details |
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- Model type: **XGBRanker** (`objective='rank:pairwise'`) |
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- Features: ~1000 numeric ingredient-parent ratio features + basic nutrition/time features |
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- Train/test split: 80/20 (by recipe ID) |
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- Evaluation metric: NDCG@5, NDCG@10 |
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### Evaluation |
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The baseline achieves **very high NDCG scores (95%+)**, because training and evaluation rely on synthetic signals that align perfectly with the ranking structure. |
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### Intended Use |
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Serve as a **sanity check** and upper bound for ranking performance, not for deployment. |
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### Limitations |
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- Unrealistically clean preference structure |
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- No user differentiation |
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- Inflated metrics due to synthetic evaluation |
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--- |
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## Model Category 2: Rule-enhanced Cold Start Models (User1–User5) |
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### Purpose |
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Capture user-specific dietary preferences and ranking heuristics using richer rule sets, leading to more diverse recommendation patterns across different users. |
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### Data Sources |
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- Cleaned Food.com dataset (~180k recipes) |
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- 5,000 cold-start synthetic interactions per user profile |
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- Additional unselected (negative) samples included to simulate realistic cold-start scenarios |
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### Model |
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- Model type: **XGBRanker** (scratch-trained) |
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- Training objective: `rank:pairwise` |
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- Feature space: |
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- Ingredient-parent coverage ratios (~1000 parent nodes) |
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- Nutrition features: protein, calories, cost, cooking time |
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- User preference weights: protein/time/cost |
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- Dietary tag filters and exclusion rules |
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### Training Setup |
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- Train/valid/test split: 70/15/15 by recipe ID per profile |
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- No fine-tuning between profiles; each profile trained independently |
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- Evaluation metric: NDCG@5 and NDCG@10 |
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### Evaluation Results |
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| User Profile | NDCG@5 | NDCG@10 | |
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|-------------|--------|---------| |
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| user1 | 0.4400 | 0.4400 | |
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| user2 | 0.4342 | 0.4342 | |
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| user3 | 0.4179 | 0.4179 | |
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| user4 | 0.1651 | 0.1651 | |
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| user5 | 0.4607 | 0.4607 | |
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**Note:** User4 has very restrictive dietary preferences, resulting in very few matching recipes and inherently lower achievable NDCG. |
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Although these NDCG values are lower than the baseline, this is expected for several reasons: |
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- The cold-start datasets contain a large proportion of unselected recipes, leading to sparse positive signals. |
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- More complex preference rules increase variability and reduce alignment with NDCG’s single-label relevance assumptions. |
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- The models now produce more differentiated ranking behaviors across user profiles, which aligns with the intended personalization goals. |
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--- |
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## Model Selection Justification |
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- **XGBRanker** was chosen for all models due to its effectiveness on structured tabular data, fast training time, and compatibility with large feature spaces (1000+ ingredients). |
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- The **baseline model** acts as a clean control, providing an upper bound on achievable NDCG under idealized preferences. |
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- The **rule-enhanced models** trade some raw NDCG performance for greater personalization fidelity, which is critical in multi-user recommendation contexts. |
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--- |
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## Evaluation Methodology |
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- Metric: NDCG@5 and NDCG@10 on held-out cold-start samples |
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- Each user model evaluated independently |
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- Negative samples retained to approximate real-world recommendation class imbalance |
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--- |
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## Intended Uses and Limitations |
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**Intended Uses** |
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- Multi-profile recipe recommendation |
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- Studying personalization behaviors under sparse feedback |
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- Cold-start scenarios for new users |
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**Limitations** |
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- Synthetic user interactions do not perfectly reflect real-world feedback |
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- NDCG is not well aligned with multi-rule personalization behavior |
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- User4 performance is limited by scarcity of relevant recipes |
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--- |
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## Risks and Bias |
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The models are trained on the Food.com dataset, which has known biases: |
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- **Regional bias**: Western and American cuisines dominate the dataset, leading to potential under-representation of other regions. |
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- **Popularity bias**: Highly rated or frequently interacted recipes are over-represented. |
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- **Cold-start leakage risk**: Although user interactions are synthetic, overlapping ingredient-parent structures between train/test may create mild information leakage, potentially inflating baseline metrics. |
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These biases may affect recommendation diversity and fairness across different cuisines or dietary groups. |
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## Cost and Latency |
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All models are based on **XGBRanker**, which runs efficiently on CPU: |
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- **Inference latency**: Approximately 1–5 ms per recipe for ranking (measured on a laptop CPU, single thread). |
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- **Training cost**: Training each user profile model on 5,000 interactions takes less than 2 minutes on CPU. |
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The approach is designed for real-time personalization in lightweight interfaces (e.g., Hugging Face Spaces). |
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## Usage Disclosure |
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**Intended Uses** |
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- Academic and educational research on personalized recommendation |
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- Cold-start personalization experiments |
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- Recipe recommendation for diverse dietary profiles |
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**Not Intended For** |
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- Medical or dietary decision-making |
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- Real-world deployment without additional bias mitigation |
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- High-stakes personalization where fairness across demographic groups is critical |
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--- |
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## Citation |
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Tang, Xinxuan. Personalized Recipe Ranking Models. 2025. |
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