Developing a restaurant recommended system via the Vietnamese food image classification

Viet Hoang Pham, Anh Thai Nguyen, Bao The Phung, Truong Ho-Viet Phan


A recommendation system is a system that recommends products and services to users based on daily online searching habits. The recommender system is applied in many fields such as job searching, health care, education, music, and tourism. However, few studies have combined computer vision and collaborative filtering to build a restaurant recommendation system in the tourism sector. In this study, we presented a solution to build a restaurant recommendation system through Vietnamese food image classification. First, we used ResNet-34 which is a variant of the convolutional neural network to classify Vietnamese food images. Then, the system applied the alternative least square technique in matrix factorization and Apache Spark in distributed computing to train the restaurant location dataset. The output was the most relevant restaurant places list to show many choices to users. The experimental datasets included the Vietnamese image and the restaurant location datasets that were collected from and websites. For image classification task evaluation, we compared ResNet-34 to variants of ResNet. For the restaurant recommendation task evaluation, we compared alternative least squares with k-nearest neighbor. The comparison results show that the proposed solution is better than traditional popular models.


Alternative least square; Apache Spark; Image classification; Recommendation system; ResNet

Full Text:



Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).