# Enhancing Personalized Recipe Recommendation Through Multi-Class Classification

arXiv: https://arxiv.org/abs/2409.10267 · HTML: https://mlsanity.com/paper/2409.10267/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 16 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Stopwords in Technical Language Processing](https://mlsanity.com/paper/2006.02633/) (2020)
- [Food Recommender Systems: Important Contributions, Challenges and Future Research Directions](https://mlsanity.com/paper/1711.02760/) (2017)
- [Network visualizations with Pyvis and VisJS](https://mlsanity.com/paper/2006.04951/) (2020)

## Built on by (most cited first, 1 of 1)

- [S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipe](https://mlsanity.com/paper/2507.09101/) (2025)

## Abstract (arXiv)

This paper intends to address the challenge of personalized recipe recommendation in the realm of diverse culinary preferences. The problem domain involves recipe recommendations, utilizing techniques such as association analysis and classification. Association analysis explores the relationships and connections between different ingredients to enhance the user experience. Meanwhile, the classification aspect involves categorizing recipes based on user-defined ingredients and preferences. A unique aspect of the paper is the consideration of recipes and ingredients belonging to multiple classes, recognizing the complexity of culinary combinations. This necessitates a sophisticated approach to classification and recommendation, ensuring the system accommodates the nature of recipe categorization. The paper seeks not only to recommend recipes but also to explore the process involved in achieving accurate and personalized recommendations.