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Related Experiment Video

Updated: Jan 9, 2026

Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Adaptive dynamic hypergraph learning for ingredient aware food recommendation.

Yazeed Alkhrijah¹, Abbas N Talib², Narinderjit Singh Sawaran Singh³

¹Department of Electrical Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Kingdom of Saudi Arabia.

Scientific Reports

|December 5, 2025

Summary

This study introduces a new food recommendation system, FRMADHG, that uses a dynamic hypergraph to better understand user preferences and ingredient relationships. It significantly improves recommendation accuracy and provides transparent, ingredient-level explanations.

Keywords:

Adaptive Attention Explainable AI Food Nutrition Improvement Food Recommendation Multi-Objective Learning Tripartite Hypergraphs

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Area of Science:

Artificial Intelligence
Computer Science
Data Science

Background:

Traditional food recommendation systems struggle with complex user-food-ingredient interactions.
Existing methods like collaborative filtering and Graph Neural Networks oversimplify culinary relationships.
Current hypergraph approaches lack adaptability to dynamic user preferences and ingredient semantics.

Purpose of the Study:

To propose FRMADHG (Food Recommendation with Multi-objective Adaptive Dynamic Hypergraph), a novel framework for enhanced food recommendations.
To capture higher-order interactions using a tripartite hypergraph connecting users, foods, and ingredients.
To provide multi-granular explainability for transparent dietary decisions.

Main Methods:

Developed a complexity-aware adaptive attention mechanism for dynamic weighting.
Implemented type-specific embedding propagation rules for distinct semantic roles.
Utilized dynamic Laplacian construction that evolves during training.
Employed a multi-objective learning strategy combining triplet ranking, contrastive learning, and regularization.

Main Results:

FRMADHG achieved significant improvements: 19.8% gain in Precision@10 and 18.7% enhancement in Recall@10 over state-of-the-art methods.
Ablation studies confirmed the impact of dynamic hypergraph construction (12.4%), adaptive attention (11.8%), and contrastive learning (9.7%).
User studies validated the effectiveness of ingredient-level explanations in building trust and satisfaction.

Conclusions:

FRMADHG effectively models complex culinary interactions through its adaptive dynamic hypergraph approach.
The framework offers superior recommendation performance and explainability compared to existing methods.
Ingredient-level explanations enhance user trust and satisfaction in food recommendation systems.