Design of Hybrid Recommendation Algorithm in Online Shopping System  

作　　者：Yingchao Wang Yuanhao Zhu Zongtian Zhang Huihuang Liu Peng Guo 

机构地区：[1]Hunan University of Finance and Economics,Changsha,China [2]University Malaysia Sabah,Kota Kinabalu,Malaysia

出　　处：《Journal of New Media》2021年第4期119-128,共10页新媒体杂志（英文）

基　　金：This work was supported in part by the National Natural Science Foundation of China,Grant No.72073041;Open Foundation for the University Innovation Platform in the Hunan Province,Grant No.18K103;2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property.Hunan Provincial Key Laboratory of Finance&Economics Big Data Science and Technology;2020 Hunan Provincial Higher Education Teaching Reform Research Project under Grant HNJG-2020-1130,HNJG-2020-1124;2020 General Project of Hunan Social Science Fund under Grant 20B16;Scientific Research Project of Education Department of Hunan Province(Grand No.20K021);Social Science Foundation of Hunan Province(Grant No.17YBA049).

摘　　要：In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to users.Therefore,the accuracy of the recommendation algorithm is a primary issue.So far,there are three mainstream recommendation algorithms,content-based recommendation algorithms,collaborative filtering algorithms and hybrid recommendation algorithms.Content-based recommendation algorithms and collaborative filtering algorithms have their own shortcomings.The content-based recommendation algorithm has the problem of the diversity of recommended items,while the collaborative filtering algorithm has the problem of data sparsity and scalability.On the basis of these two algorithms,the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better services.This article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests,and then combine the collaborative filtering algorithm to establish a potential interest model,mix the existing and potential interests,and calculate with the candidate search content set.The similarity gets the recommendation list.

关 键 词：Recommendation algorithm hybrid recommendation algorithm content-based recommendation algorithm collaborative filtering algorithm 

分 类 号：TN9[电子电信—信息与通信工程]