Recommendation System Based on Perceptron and Graph Convolution Network

作     者：Zuozheng Lian Yongchao Yin Haizhen Wang 

作者机构：College of Computer and Control EngineeringQiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and AnalysisQiqihar UniversityQiqihar161006China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第79卷第6期

页      面：3939-3954页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081203[工学-计算机应用技术] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0703[理学-化学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Fundamental Research Funds for Higher Education Institutions of Heilongjiang Province(145209126) the Heilongjiang Province Higher Education Teaching Reform Project under Grant No.SJGY20200770 

主　　题：Recommendation system graph convolution network attention mechanism multi-layer perceptron 

摘      要：The relationship between users and items,which cannot be recovered by traditional techniques,can be extracted by the recommendation algorithm based on the graph convolution *** current simple linear combination of these algorithms may not be sufficient to extract the complex structure of user interaction *** paper presents a new approach to address such issues,utilizing the graph convolution network to extract association *** proposed approach mainly includes three modules:Embedding layer,forward propagation layer,and score prediction *** embedding layer models users and items according to their interaction information and generates initial feature vectors as input for the forward propagation *** forward propagation layer designs two parallel graph convolution networks with self-connections,which extract higher-order association relevance from users and items separately by multi-layer graph ***,the forward propagation layer integrates the attention factor to assign different weights among the hop neighbors of the graph convolution network fusion,capturing more comprehensive association relevance between users and items as input for the score prediction *** score prediction layer introduces MLP(multi-layer perceptron)to conduct non-linear feature interaction between users and items,***,the prediction score of users to items is *** recall rate and normalized discounted cumulative gain were used as evaluation *** proposed approach effectively integrates higher-order information in user entries,and experimental analysis demonstrates its superiority over the existing algorithms.