# Learning Personal Food Preferences via Food Logs Embedding

arXiv: https://arxiv.org/abs/2110.15498 · HTML: https://mlsanity.com/paper/2110.15498/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [Food Recommender Systems: Important Contributions, Challenges and Future Research Directions](https://mlsanity.com/paper/1711.02760/) (2017)
- [Diet2Vec: Multi-scale analysis of massive dietary data](https://mlsanity.com/paper/1612.00388/) (2016)

## Built on by (most cited first, 2 of 2)

- [NutriOrion: A Hierarchical Multi-Agent Framework for Personalized Nutrition Intervention Grounded in Clinical Guidelines](https://mlsanity.com/paper/2602.18650/) (2026)
- [MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention](https://mlsanity.com/paper/2606.10120/) (2026)

## Abstract (arXiv)

Diet management is key to managing chronic diseases such as diabetes. Automated food recommender systems may be able to assist by providing meal recommendations that conform to a user's nutrition goals and food preferences. Current recommendation systems suffer from a lack of accuracy that is in part due to a lack of knowledge of food preferences, namely foods users like to and are able to eat frequently. In this work, we propose a method for learning food preferences from food logs, a comprehensive but noisy source of information about users' dietary habits. We also introduce accompanying metrics. The method generates and compares word embeddings to identify the parent food category of each food entry and then calculates the most popular. Our proposed approach identifies 82% of a user's ten most frequently eaten foods. Our method is publicly available on (https://github.com/aametwally/LearningFoodPreferences)