# Food Recommendation using Ontology and Heuristics

arXiv: https://arxiv.org/abs/1312.1448 · HTML: https://mlsanity.com/paper/1312.1448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2013.

## Built on by (most cited first, 3 of 3)

- [Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph](https://mlsanity.com/paper/2101.01775/) (2021)
- [Semantic Modeling for Food Recommendation Explanations](https://mlsanity.com/paper/2105.01269/) (2021)
- [Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations](https://mlsanity.com/paper/2208.04223/) (2022)

## Abstract (arXiv)

Recommender systems are needed to find food items of ones interest. We review recommender systems and recommendation methods. We propose a food personalization framework based on adaptive hypermedia. We extend Hermes framework with food recommendation functionality. We combine TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.