# Can a Chatbot Determine My Diet?: Addressing Challenges of Chatbot Application for Meal Recommendation

arXiv: https://arxiv.org/abs/1802.09100 · HTML: https://mlsanity.com/paper/1802.09100/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2018.

## Built on by (most cited first, 5 of 5)

- [Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph](https://mlsanity.com/paper/2104.14899/) (2021)
- [Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering](https://mlsanity.com/paper/2110.11592/) (2021)
- [Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service](https://mlsanity.com/paper/2108.00724/) (2021)
- [Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images](https://mlsanity.com/paper/2108.03788/) (2021)
- [When LLMs Can't Help: Real-World Evaluation of LLMs in Nutrition](https://mlsanity.com/paper/2511.20652/) (2025)

## Abstract (arXiv)

Poor nutrition can lead to reduced immunity, increased susceptibility to disease, impaired physical and mental development, and reduced productivity. A conversational agent can support people as a virtual coach, however building such systems still have its associated challenges and limitations. This paper describes the background and motivation for chatbot systems in the context of healthy nutrition recommendation. We discuss current challenges associated with chatbot application, we tackled technical, theoretical, behavioural, and social aspects of the challenges. We then propose a pipeline to be used as guidelines by developers to implement theoretically and technically robust chatbot systems.