# Market2Dish: Health-aware Food Recommendation

arXiv: https://arxiv.org/abs/2012.06416 · HTML: https://mlsanity.com/paper/2012.06416/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation](https://mlsanity.com/paper/2412.08847/) (2024)
- [NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering](https://mlsanity.com/paper/2510.09854/) (2025)
- [Scalable Incomplete Multi-View Clustering with Structure Alignment](https://mlsanity.com/paper/2308.16541/) (2023)
- [Efficient Multi-View Graph Clustering with Local and Global Structure Preservation](https://mlsanity.com/paper/2309.00024/) (2023)
- [Recommender Systems for Sustainability: Overview and Research Issues](https://mlsanity.com/paper/2412.03620/) (2024)
- [RecipeGen: A Benchmark for Real-World Recipe Image Generation](https://mlsanity.com/paper/2503.05228/) (2025)
- [S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipe](https://mlsanity.com/paper/2507.09101/) (2025)
- [RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation](https://mlsanity.com/paper/2506.06733/) (2025)

## Abstract (arXiv)

With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, cooking assistance, or the nutrition and calorie estimation of dishes, ignoring the personalized health-aware food recommendation. Therefore, in this work, we present a personalized health-aware food recommendation scheme, namely Market2Dish, mapping the ingredients displayed in the market to the healthy dishes eaten at home. The proposed scheme comprises three components, namely recipe retrieval, user-health profiling, and health-aware food recommendation. In particular, recipe retrieval aims to acquire the ingredients available to the users, and then retrieve recipe candidates from a large-scale recipe dataset. User health profiling is to characterize the health conditions of users by capturing the textual health-related information crawled from social networks. Specifically, to solve the issue that the health-related information is extremely sparse, we incorporate a word-class interaction mechanism into the proposed deep model to learn the fine-grained correlations between the textual tweets and pre-defined health concepts. For the health-aware food recommendation, we present a novel category-aware hierarchical memory network-based recommender to learn the health-aware user-recipe interactions for better food recommendation. Moreover, extensive experiments demonstrate the effectiveness of the health-aware food recommendation scheme.