Title: Intelligent products' recommendation system based on machine learning algorithm combined with visual features extraction
Authors: Jianzhong Yang; Huirong Chen; Xianyang Li
Addresses: College of Electronic and Information Engineer, Beibu Gulf, Qinzhou, Guangxi, 535011, China ' College of Resource and Environment, Beibu Gulf, Qinzhou, Guangxi, 535011, China ' College of Electronic and Information Engineer, Beibu Gulf, Qinzhou, Guangxi, 535011, China
Abstract: This paper examines how to improve the problems of low user satisfaction and low recommendation efficiency. A method of intelligent recommendation system for products, taking nixing Tao products as an example, was proposed based on gradient boosting decision tree algorithm in machine learning and Zernike features extraction of machine vision. The overall structure of the recommendation system for products was formed with user shopping module, system management module, database module and visualisation module. The key features of products were extracted by Zernike moment, and they were analysed by clustering in order to obtain the association rules between products and users. We embedded the association rules into recommendation model based on gradient extension decision tree algorithm. The experimental results show our method had short recommendation time and good user satisfaction.
Keywords: machine learning; machine vision; gradient boosting decision tree; feature extraction; sassociation rules; recommendation model.
International Journal of Biometrics, 2022 Vol.14 No.2, pp.125 - 137
Received: 04 Sep 2020
Accepted: 29 Oct 2020
Published online: 07 Apr 2022 *