# Food Recommendation: Framework, Existing Solutions and Challenges

arXiv: https://arxiv.org/abs/1905.06269 · HTML: https://mlsanity.com/paper/1905.06269/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2019.

## Built on by (most cited first, 21 of 21)

- [FoodLMM: A Versatile Food Assistant using Large Multi-modal Model](https://mlsanity.com/paper/2312.14991/) (2023)
- [ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework](https://mlsanity.com/paper/2403.00781/) (2024)
- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition](https://mlsanity.com/paper/2107.06538/) (2021)
- [On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition](https://mlsanity.com/paper/2205.13282/) (2022)
- [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024)
- [A review on vision-based analysis for automatic dietary assessment](https://mlsanity.com/paper/2108.02947/) (2021)
- [SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food Detection](https://mlsanity.com/paper/2310.04689/) (2023)
- [IMVB7t: A Multi-Modal Model for Food Preferences based on Artificially Produced Traits](https://mlsanity.com/paper/2412.16807/) (2024)
- [Cross-lingual Adaptation for Recipe Retrieval with Mixup](https://mlsanity.com/paper/2205.03891/) (2022)
- [Feature-Suppressed Contrast for Self-Supervised Food Pre-training](https://mlsanity.com/paper/2308.03272/) (2023)
- [A topological analysis of the space of recipes](https://mlsanity.com/paper/2406.09445/) (2024)
- [RecipeGen: A Benchmark for Real-World Recipe Image Generation](https://mlsanity.com/paper/2503.05228/) (2025)
- [World Food Atlas Project](https://mlsanity.com/paper/2504.18727/) (2025)
- [From "What to Eat?" to Perfect Recipe: ChefMind's Chain-of-Exploration for Ambiguous User Intent in Recipe Recommendation](https://mlsanity.com/paper/2509.18226/) (2025)
- [MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model](https://mlsanity.com/paper/2210.08266/) (2022)
- [A Food Recommender System in Academic Environments Based on Machine Learning Models](https://mlsanity.com/paper/2306.16528/) (2023)
- [Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation](https://mlsanity.com/paper/2311.02938/) (2023)
- [Building FKG.in: a Knowledge Graph for Indian Food](https://mlsanity.com/paper/2409.00830/) (2024)
- [Enhancing FKG.in: automating Indian food composition analysis](https://mlsanity.com/paper/2412.05248/) (2024)
- [RecipeGen: A Step-Aligned Multimodal Benchmark for Real-World Recipe Generation](https://mlsanity.com/paper/2506.06733/) (2025)

## Abstract (arXiv)

A growing proportion of the global population is becoming overweight or obese, leading to various diseases (e.g., diabetes, ischemic heart disease and even cancer) due to unhealthy eating patterns, such as increased intake of food with high energy and high fat. Food recommendation is of paramount importance to alleviate this problem. Unfortunately, modern multimedia research has enhanced the performance and experience of multimedia recommendation in many fields such as movies and POI, yet largely lags in the food domain. This article proposes a unified framework for food recommendation, and identifies main issues affecting food recommendation including building the personal model, analyzing unique food characteristics, incorporating various context and domain knowledge. We then review existing solutions for these issues, and finally elaborate research challenges and future directions in this field. To our knowledge, this is the first survey that targets the study of food recommendation in the multimedia field and offers a collection of research studies and technologies to benefit researchers in this field.