Food Recommender System: Methods, Challenges, and Future Research Directions
Food Recommender System: Methods, Challenges, and Future Research Directions |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-5 |
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| Year of Publication : 2025 | ||
| Author : Zinkal Patel, Hina Jignesh Chokshi |
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| DOI : 10.14445/22315381/IJETT-V73I5P123 | ||
How to Cite?
Zinkal Patel, Hina Jignesh Chokshi, "Food Recommender System: Methods, Challenges, and Future Research Directions," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.267-276, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P123
Abstract
In recent years, the food recommender system has drawn attention to the growing need for individualized meal recommendations, dietary planning, and restaurant recommendations. The food recommender system has become increasingly important in a digital world, allowing users to select food dishes based on their preferences, dietary restrictions, and contextual factors. The demand for personalized food recommendations increased significantly with the increasing popularity of digital food delivery services and restaurant recommendation platforms. The recommendation system utilized many machine learning algorithms and various artificial intelligence techniques for mining and analysing food attributes and user preferences from past ordered and contextual factors to recommend relevant recommendations. This paper aims to inspect different food recommender system types, such as Content-Based Filtering, Collaborative Filtering, and Hybrid Filtering Models. It also explores the key challenges inherent in the food recommender system, such as sparse data, the cold start issue, cultural problems, and nutritional quality. The paper also focuses on new developments and possible options for future study, including explainable AI, multi-model data fusion, and the integration of health considerations into recommendation models. Additionally, it identifies research gaps and presents the integration of explainable AI, multimodal learning, and health-aware recommendations to further enhance the effectiveness of food recommendation systems. By addressing these challenges and exploring emerging technologies, this study aims to pave the way for more accurate, transparent, and user-centric food recommendation models. It also provides a comprehensive understanding of food recommender systems and their role in enhancing user experience, health-conscious eating, and personalized dining choices.
Keywords
Collaborative Filtering, Content-Based Filtering, Hybrid Filtering, Memory Based Filtering Technique, Model-Based Filtering Technique, Recommender System.
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