基于用户行为特征的网络教学云平台资源个性化推荐方法  

Personalized recommendation method of cloud platform resources for network teaching based on user behavior characteristics

作　　者：赵莉苹 ZHAO Liping(School of Information Engineering,Zhengzhou University of Science and Technology,Zhengzhou 450064,China)

机构地区：[1]郑州科技学院信息工程学院,河南郑州450064

出　　处：《无线互联科技》2024年第18期116-118,共3页Wireless Internet Science and Technology

基　　金：校级教改项目,项目名称:云平台支持下计算机类专业学生创新能力培养研究,项目编号:2024JGZD12。

摘　　要：针对网络教学云平台的数据量大导致推荐的相关度低的问题,文章提出基于用户行为特征的网络教学云平台资源个性化推荐方法。该方法通过采集历史数据,筛选关键特征,结合K-中心聚类算法挖掘用户行为特征,建立与资源特征关联规则,结合偏好因子,为用户生成资源个性化推荐列表。实验测试结果表明,该方法推荐相关度高,满足平台实际应用需求。The problem of low relevance of recommendations due to the large amount of data on online teaching cloud platforms.This article proposes a personalized recommendation method for online teaching cloud platform resources based on user behavior characteristics.By collecting historical data to filter key features,combining K-center clustering algorithm to mine user behavior characteristics,establishing association rules with resource features,and combining preference factors,a personalized resource recommendation list is generated for users.Experimental testing shows that this method has high recommendation relevance and meets the practical application needs of the platform.

关 键 词：资源推荐 个性化推荐 网络教学云平台 用户行为特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]