A Recipe Recommender System Using Natural Lanuguage Processing and Similarity Measures
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Date
2024-11-25
Authors
Bukola Badeji-Ajisafe
Betsy G. Otoyo
Temiloluwa A. Adebola
Abimbola O. Ajibade
Olanike C. Akinduyite
Stephen. E Obamiyi
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MIRG
Abstract
Personalized recipe recommendation has become a compelling need, driven by the motivation to develop intelligent systems that can match user preferences with the most related suggestions. This paper presents a recipe recommender system in which several word-embedding models—namely, Word2Vec, FastText, and Sentence Encoder (Mini LM)—are evaluated for obtaining high semantic similarity between user input and recipes in the dataset. Cosine similarity is used as the primary metric for measuring how close user inputs are to the existing recipes. The results show that the Word2Vec model performs best and has good understanding of language patterns related to the recommendation of recipes in a more precise manner. The study hence justifies using Word2Vec as an asset for future personalized recommendation systems.
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Bukola B., Betsy G. O., Temiloluwa A. A., Abimbola O. A., Olanike C. A. & Stephen E. O. (2024). “A Recipe Recommender System Using Natural Lanuguage Processing and Similarity Measures". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2024), pp. 77-83, MIRG