Thai food recommendation system using hybrid of particle swarm optimization and K-means algorithm

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dc.contributor.authorPuraram, Tanakorn
dc.contributor.authorChaovalit, Pimwadee
dc.contributor.authorPeethong, Apatha
dc.contributor.authorTiyanunti, Pongsak
dc.contributor.authorCharoensiriwath, Supiya
dc.contributor.authorKimpan, Warangkhana
dc.date.accessioned2026-08-06T10:32:27Z
dc.date.available2026-08-06T10:32:27Z
dc.date.issued2021-04-23
dc.description.abstractA food recommendation system is an information filtering tool that helps suggest appropriate food menus to users based on their dietary behavior, nutrition, health, or activity. In this paper, a hybrid method of Particle Swarm Optimization (PSO) and K-Means algorithm is proposed to improve the user's dietary behavior clustering and using Principal Component Analysis (PCA) to reduce the data dimension. Moreover, the User-Based Collaborative Filtering technique is used to predict the rating of relevant Thai food menus and recommendation. The experimental result shows the hybrid method improves the clustering performance from 3 models: Hierarchical Clustering, K-Means, and K-Means with PCA, in terms of silhouette coefficient score. In addition, the hybrid method improves the Davies-Bouldin index score by 44%, 19%, and 17% compared to those models, respectively. The rating prediction result shows the hybrid method outperforms the other methods.
dc.identifier.citationACM International Conference Proceeding Series, 90-95, 2021
dc.identifier.doi10.1145/3468891.3468904
dc.identifier.other2-s2.0-85114667164
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/11994
dc.sourceACM International Conference Proceeding Series
dc.subjectCollaborative Filtering
dc.subjectK-Means
dc.subjectParticle Swarm Optimization
dc.subjectThai food Recommendation
dc.titleThai food recommendation system using hybrid of particle swarm optimization and K-means algorithm
dc.typeConference Paper

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