Food Recommender System: A Review on Techniques, Datasets and Evaluation Metrics


Chow, Yi Ying and Haw, Su Cheng and Naveen, Palanichamy and Anaam, Elham Abdulwahab and Mahdin, Hairulnizam (2023) Food Recommender System: A Review on Techniques, Datasets and Evaluation Metrics. Journal of System and Management Sciences, 13 (5). ISSN 1816-6075, 1818-0523

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With the rise of digital platforms and the availability of large amounts of data, food recommender systems have become a powerful tool for helping people discover new and delicious meals. Today, these systems use algorithms and machine learning models to analyze ingredients and recommend meals based on factors such as cuisine, dietary restrictions, and ingredient compatibility. Hence, this paper aims to review the various recommendation techniques employed in the food recommender system. We also discussthe various algorithms that are used in meal recommender systems, including collaborative filtering, content-based filtering, and hybrid approaches. Overall, this paper provides a comprehensive overview of the current state-of-the-art meal recommender systems and to identify the opportunities for future enhancement and development in this field.

Item Type: Article
Uncontrolled Keywords: food recommender, meal recommender, recommender system, collaborative filtering, content-based, hybrid approaches
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 31 Oct 2023 08:19
Last Modified: 31 Oct 2023 08:19


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