# Personalized Food Recommendation as Constrained Question Answering over a Large-scale Food Knowledge Graph

arXiv: https://arxiv.org/abs/2101.01775 · HTML: https://mlsanity.com/paper/2101.01775/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 56 references that are in the map.
- First posted on arXiv 5 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Large-scale Simple Question Answering with Memory Networks](https://mlsanity.com/paper/1506.02075/) (2015)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [Question Answering with Subgraph Embeddings](https://mlsanity.com/paper/1406.3676/) (2014)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases](https://mlsanity.com/paper/1903.02188/) (2019)
- [Open Question Answering with Weakly Supervised Embedding Models](https://mlsanity.com/paper/1404.4326/) (2014)
- [Food Recommendation: Framework, Existing Solutions and Challenges](https://mlsanity.com/paper/1905.06269/) (2019)
- [Food Recommender Systems: Important Contributions, Challenges and Future Research Directions](https://mlsanity.com/paper/1711.02760/) (2017)
- [Eating Healthier: Exploring Nutrition Information for Healthier Recipe Recommendation](https://mlsanity.com/paper/2003.07027/) (2020)
- [Food Recommendation using Ontology and Heuristics](https://mlsanity.com/paper/1312.1448/) (2013)

## Built on by (most cited first, 17 of 17)

- [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024)
- [Applications of knowledge graphs for food science and industry](https://mlsanity.com/paper/2107.05869/) (2021)
- [Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes](https://mlsanity.com/paper/2302.05752/) (2023)
- [Learning to Substitute Ingredients in Recipes](https://mlsanity.com/paper/2302.07960/) (2023)
- [Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning](https://mlsanity.com/paper/2110.12679/) (2021)
- [Fine-tuning Language Models for Recipe Generation: A Comparative Analysis and Benchmark Study](https://mlsanity.com/paper/2502.02028/) (2025)
- [Learning Personal Food Preferences via Food Logs Embedding](https://mlsanity.com/paper/2110.15498/) (2021)
- [KERL: Knowledge-Enhanced Personalized Recipe Recommendation using Large Language Models](https://mlsanity.com/paper/2505.14629/) (2025)
- [Food Recommendation as Language Processing (F-RLP): A Personalized and Contextual Paradigm](https://mlsanity.com/paper/2402.07477/) (2024)
- [A Neuro-Symbolic Approach to Monitoring Salt Content in Food](https://mlsanity.com/paper/2404.01182/) (2024)
- [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)
- [Leveraging Clinical Context for User-Centered Explainability: A Diabetes Use Case](https://mlsanity.com/paper/2107.02359/) (2021)
- [VRKG4Rec: Virtual Relational Knowledge Graphs for Recommendation](https://mlsanity.com/paper/2204.01089/) (2022)
- [RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?](https://mlsanity.com/paper/2308.04579/) (2023)
- [Charting the Future of Scholarly Knowledge with AI: A Community Perspective](https://mlsanity.com/paper/2509.02581/) (2025)
- [Semantic Reasoning in Medicine: The Role of Knowledge Graphs Across Five Key Domains](https://mlsanity.com/paper/2606.15155/) (2026)
- [Nutri-ATLAS: Embodied Agent for Tabulated Lookup and Assistance for Smarter nutrition](https://mlsanity.com/paper/2609.32803/) (2026)

## Abstract (arXiv)

Food recommendation has become an important means to help guide users to adopt healthy dietary habits. Previous works on food recommendation either i) fail to consider users' explicit requirements, ii) ignore crucial health factors (e.g., allergies and nutrition needs), or iii) do not utilize the rich food knowledge for recommending healthy recipes. To address these limitations, we propose a novel problem formulation for food recommendation, modeling this task as constrained question answering over a large-scale food knowledge base/graph (KBQA). Besides the requirements from the user query, personalized requirements from the user's dietary preferences and health guidelines are handled in a unified way as additional constraints to the QA system. To validate this idea, we create a QA style dataset for personalized food recommendation based on a large-scale food knowledge graph and health guidelines. Furthermore, we propose a KBQA-based personalized food recommendation framework which is equipped with novel techniques for handling negations and numerical comparisons in the queries. Experimental results on the benchmark show that our approach significantly outperforms non-personalized counterparts (average 59.7% absolute improvement across various evaluation metrics), and is able to recommend more relevant and healthier recipes.