Recipes Recommendation System using Machine Learning

Abstract

With the increasing popularity of online cooking platforms and the vast availability of recipe data, personalized recipe recommendation systems have become an essential tool to enhance user experience. This research presents a Machine Learning-based Recipe Recommender System that suggests the top five most relevant recipes based on user-provided ingredients or a recipe name. The system leverages Natural Language Processing (NLP) techniques to extract and analyses key features from a large recipe dataset, including ingredient lists, recipe titles, and preparation steps. A content-based filtering approach, enhanced by vectorization techniques such as TF-IDF and cosine similarity, is used to find recipes most like the user\'s input. Our model effectively narrows down recipe options by matching user preferences with existing recipes, providing personalized and efficient culinary suggestions. The proposed system demonstrates high accuracy in aligning with user intent and offers a scalable solution for integration into cooking apps or digital kitchen assistants.

Introduction

The digital transformation has changed how people find and prepare food, but the abundance of online recipes often overwhelms users. Traditional search engines fail to provide personalized, ingredient-specific recommendations, causing decision fatigue. To improve this, recipe recommender systems use machine learning to suggest personalized dishes based on user input like ingredients or recipe names.

This paper presents a hybrid recipe recommendation system combining content-based filtering (using TF-IDF and cosine similarity on recipe text data) and collaborative filtering (user behavior analysis) to deliver the top five personalized recipe suggestions. The system is lightweight, scalable, and designed for integration into web or mobile apps, enhancing meal planning and reducing food waste.

The literature review discusses existing approaches: content-based filtering, collaborative filtering, hybrid models, and advanced machine learning techniques including neural networks and knowledge graphs. Despite progress, challenges like data quality and dynamic preferences persist.

The methodology details a hybrid recommendation model, data collection and preprocessing, machine learning algorithm implementation (e.g., KNN, matrix factorization), system evaluation metrics, and frontend/backend development.

A data flow diagram outlines the system’s processes, from user input to recipe recommendation output. Evaluation on a 10,000-recipe dataset showed effective personalized recommendations using TF-IDF vectorization and cosine similarity.

Conclusion

The proposed recipe recommender system effectively leverages machine learning techniques to provide personalized recipe suggestions based on user input, such as ingredients or recipe names. By applying natural language processing and similarity-based algorithms, the system recommends the top five most relevant recipes, enhancing user experience in meal planning and ingredient utilization. This approach not only improves convenience but also promotes smarter cooking habits. Future improvements may include the integration of user preferences, collaborative filtering, and real-time feedback to further increase the accuracy and personalization of recommendations.

References

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Copyright

Copyright © 2025 Vishnu Prasad Verma, Suman Kashyap, Fuleshwari Senger. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.