# Food Recommender Systems: Important Contributions, Challenges and Future Research Directions

arXiv: https://arxiv.org/abs/1711.02760 · HTML: https://mlsanity.com/paper/1711.02760/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## 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)
- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [Agentic AI Framework for Individuals with Disabilities and Neurodivergence: A Multi-Agent System for Healthy Eating, Daily Routines, and Inclusive Well-Being](https://mlsanity.com/paper/2511.22737/) (2025)
- [Learning Personal Food Preferences via Food Logs Embedding](https://mlsanity.com/paper/2110.15498/) (2021)
- [50 Ways to Bake a Cookie: Mapping the Landscape of Procedural Texts](https://mlsanity.com/paper/2210.17235/) (2022)
- [Enhancing Personalized Recipe Recommendation Through Multi-Class Classification](https://mlsanity.com/paper/2409.10267/) (2024)
- [Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits](https://mlsanity.com/paper/2110.07045/) (2021)
- [Building a Macedonian Recipe Dataset: Collection, Parsing, and Comparative Analysis](https://mlsanity.com/paper/2510.14128/) (2025)

## Abstract (arXiv)

The recommendation of food items is important for many reasons. Attaining cooking inspiration via digital sources is becoming evermore popular; as are systems, which recommend other types of food, such as meals in restaurants or products in supermarkets. Researchers have been studying these kinds of systems for many years, suggesting not only that can they be a means to help people find food they might want to eat, but also help them nourish themselves more healthily. This paper provides a summary of the state-of-the-art of so-called food recommender systems, highlighting both seminal and most recent approaches to the problem, as well as important specializations, such as food recommendation systems for groups of users or systems which promote healthy eating. We moreover discuss the diverse challenges involved in designing recsys for food, summarise the lessons learned from past research and outline what we believe to be important future directions and open questions for the field. In providing these contributions we hope to provide a useful resource for researchers and practitioners alike.