A Diet Recommendation System using TF-IDF and Extra Trees Algorithm

Authors

Boudjemaa Boudaa
Computer Science Department, University of Tiaret, Tiaret LabRI-SBA Lab., Ecole Superieure en Informatique, Sidi Bel Abbes
Aissa Hammadi
Computer Science Department, University of Tiaret, Tiaret
Kada Akermi
Computer Science Department, University of Science and Technology “Mohamed Boudiaf” USTO, Oran
DOI: https://doi.org/10.52549/ijeei.v12i4.5809
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Published: 02-12-2024 • Issue: Vol. 12 No. 4: December 2024 • Section: Regular paper

Abstract

Across the globe, there is a growing emphasis on health and lifestyle choices. However, refraining from unhealthy foods and staying active are not enough; maintaining a well-balanced diet is also essential. Recently, recommendation systems have focused on promoting healthy eating habits, tailoring suggestions for balanced diets based on some parameters like age, gender, height, weight, age, BMI and BMR. Pairing a nutritious diet with regular physical activity can aid in reaching and sustaining a healthy weight, reducing the likelihood of chronic ailments such as heart disease, and enhancing overall well-being. The present paper introduces a novel approach for constructing dietary recommendations with optimized calorie intake, using content-based filtering with the TF-IDF statistical method and machine learning with the Extra Trees algorithm. This approach can generate a dynamic diet based on the calories a person burns and other parameters including the current Body Mass Index (BMI) and BMR (Basal Metabolic Rate). The proposed approach has been tested on a new realworld diet dataset, showcasing its effectiveness in providing diverse and accurate diet recommendations compared to another content-based filtering method and other machine learning algorithms.
Keywords: Diet recommendation; Healthy food; Content-based filtering; TF-IDF; Extra Trees algorithm

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Author Biographies

Boudjemaa Boudaa
Boudjemaa Boudaa is an Associate Professor in the Department of Computer Science at Ibn Khaldoun University of Tiaret, Algeria. He received his PhD in Computer Science from Tlemcen University in 2017. He also holds a Magister degree in Computer Science from Tiaret University (2009) and an Engineering degree in Computer Science from Oran University (1995). Currently, he is a member of the "Service-Oriented Computing" research team at the LabRI-SBA Laboratory, ESI Sidi-Bel-Abbes. His research interests include Context-Aware Systems, Recommender Systems, Software Engineering, Autonomous Vehicles, Computer Vision, and Machine Learning.
Aissa Hammadi
Aissa Hammadi is currently a graduate of the Department of Computer Science at Ibn Khaldoun University of Tiaret, where he received his Bachelor’s Degree in Information Systems and Software Engineering in 2024. He plans to continue his studies by pursuing a master’s degree in software engineering. Alongside his academic pursuits, he engages in research in the field of artificial intelligence, with a particular interest in recommendation systems and machine learning.
Kada Akermi
Kada Akermi earned his Ph.D. in Computer Science from the University of Science and Technology of Oran (USTO Oran) in 2020. His doctoral research focused on artificial intelligence, autonomous vehicles and control systems. Prior to this, he completed his Master’s degree in Computer Science at the University of Ibn Khaldoun Tiaret in 2012. His academic work has significantly contributed to advancements in these fields. Dr. Akermi’s expertise encompasses a range of technologies and methodologies related to his research interests.

How to Cite

[1]
B. Boudaa, A. Hammadi, and K. Akermi, “A Diet Recommendation System using TF-IDF and Extra Trees Algorithm”, IJEEI, vol. 12, no. 4, pp. 940–949, Dec. 2024, doi: 10.52549/ijeei.v12i4.5809.