Iris314/SmartFridgeUserModels

Model Card: Personalized Recipe Ranking Models

Overview

This project implements a personalized recipe recommendation system using two model categories:

1. Scratch-trained baseline: A simple rule-based + embedding matching ranker trained on a synthetic preference dataset (no user-specific rules).
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).

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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Model Category 1: Scratch-trained Baseline

Purpose

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).

Data Sources

• Cleaned Food.com dataset (~180k recipes)
• 10,000 synthetic preference samples generated via uniform random selection

Training Details

• Model type: XGBRanker (objective='rank:pairwise')
• Features: ~1000 numeric ingredient-parent ratio features + basic nutrition/time features
• Train/test split: 80/20 (by recipe ID)
• Evaluation metric: NDCG@5, NDCG@10

Evaluation

The baseline achieves very high NDCG scores (95%+), because training and evaluation rely on synthetic signals that align perfectly with the ranking structure.

Intended Use

Serve as a sanity check and upper bound for ranking performance, not for deployment.

Limitations

• Unrealistically clean preference structure
• No user differentiation
• Inflated metrics due to synthetic evaluation

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Model Category 2: Rule-enhanced Cold Start Models (User1–User5)

Purpose

Capture user-specific dietary preferences and ranking heuristics using richer rule sets, leading to more diverse recommendation patterns across different users.

Data Sources

• Cleaned Food.com dataset (~180k recipes)
• 5,000 cold-start synthetic interactions per user profile
• Additional unselected (negative) samples included to simulate realistic cold-start scenarios

Model

• Model type: XGBRanker (scratch-trained)
• Training objective: rank:pairwise
• Feature space:
  • Ingredient-parent coverage ratios (~1000 parent nodes)
  • Nutrition features: protein, calories, cost, cooking time
  • User preference weights: protein/time/cost
  • Dietary tag filters and exclusion rules

Training Setup

• Train/valid/test split: 70/15/15 by recipe ID per profile
• No fine-tuning between profiles; each profile trained independently
• Evaluation metric: NDCG@5 and NDCG@10

Evaluation Results

User Profile	NDCG@5	NDCG@10
user1	0.4400	0.4400
user2	0.4342	0.4342
user3	0.4179	0.4179
user4	0.1651	0.1651
user5	0.4607	0.4607

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:

• The cold-start datasets contain a large proportion of unselected recipes, leading to sparse positive signals.
• More complex preference rules increase variability and reduce alignment with NDCG’s single-label relevance assumptions.
• The models now produce more differentiated ranking behaviors across user profiles, which aligns with the intended personalization goals.

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Model Selection Justification

• 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).
• The baseline model acts as a clean control, providing an upper bound on achievable NDCG under idealized preferences.
• 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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Evaluation Methodology

• Metric: NDCG@5 and NDCG@10 on held-out cold-start samples
• Each user model evaluated independently
• Negative samples retained to approximate real-world recommendation class imbalance

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Intended Uses and Limitations

Intended Uses

• Multi-profile recipe recommendation
• Studying personalization behaviors under sparse feedback
• Cold-start scenarios for new users

Limitations

• Synthetic user interactions do not perfectly reflect real-world feedback
• NDCG is not well aligned with multi-rule personalization behavior
• User4 performance is limited by scarcity of relevant recipes

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Risks and Bias

The models are trained on the Food.com dataset, which has known biases:

• Regional bias: Western and American cuisines dominate the dataset, leading to potential under-representation of other regions.
• Popularity bias: Highly rated or frequently interacted recipes are over-represented.
• 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.

These biases may affect recommendation diversity and fairness across different cuisines or dietary groups.

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Cost and Latency

All models are based on XGBRanker, which runs efficiently on CPU:

• Inference latency: Approximately 1–5 ms per recipe for ranking (measured on a laptop CPU, single thread).
• Training cost: Training each user profile model on 5,000 interactions takes less than 2 minutes on CPU.

The approach is designed for real-time personalization in lightweight interfaces (e.g., Hugging Face Spaces).

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Usage Disclosure

Intended Uses

• Academic and educational research on personalized recommendation
• Cold-start personalization experiments
• Recipe recommendation for diverse dietary profiles

Not Intended For

• Medical or dietary decision-making
• Real-world deployment without additional bias mitigation
• High-stakes personalization where fairness across demographic groups is critical

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Citation

Tang, Xinxuan. Personalized Recipe Ranking Models. 2025.