The Study of Algorithms for Food and Beverage Recommendation System on Thai Language Dataset
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Abstract
This research aims to investigate suitable algorithms for food recommendation systems on a Thai language dataset based on user preferences. Having preferred and appropriate meals can make consumers feel positive, happy, and healthy. For restaurants, this system can help improve customers’ satisfaction and loyalty, and reduce the decision time. In this work, an exploratory study was conducted on two prominent food recommendation algorithms: the content-based filtering and collaborative filtering methods. The results of the study revealed the characteristics of each method. For the content-based filtering method, Bag-of-Words (BoW) and TF-IDF technique were applied together with cosine similarity metrics. The BoW technique recommended food with more general ingredients. For the collaborative filtering method, KNN Basic, SVD, and SVD++ were employed to construct the recommendation models. The algorithms were evaluated using MAE and RMSE. The SVD++ provided a better result with the lowest MAE (0.885) and RMSE (1.105) values.
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