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Thai food recommendation system using hybrid of particle swarm optimization and K-means algorithm
Author(s)
Puraram, Tanakorn
Chaovalit, Pimwadee
Peethong, Apatha
Tiyanunti, Pongsak
Charoensiriwath, Supiya
Date Issued
April 23, 2021
Type
Conference Paper
Abstract
A 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.
Citation
ACM International Conference Proceeding Series, 90-95, 2021