教育资源个性化推荐方法研究与实现
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摘要
随着教育信息化的快速发展,网络教育资源的开发得到了国内外的高度重视,各类教育资源信息更新速度惊人。为避免教育资源浪费,提高查找效率,及时为用户推荐有效信息资料,运用Java Web开发技术,结合网络爬虫技术和预测算法分析方法,设计实现了教育资源个性化推荐系统。系统采用Java爬虫技术,获取特定教育资源网站上公开教育资源信息,作为系统研究数据来源;采用矩阵分解模型、多层感知机模型和NeuMF预测分析模型,分析处理用户特征值矩阵和教育资源特征值矩阵,关联用户特征信息与教育资源信息,得到用户-教育资源预测值;研究教育资源个性化推荐技术,按照预测值优先级,将符合用户需求的教育资源信息推荐给Web用户。经测试,系统运行稳定可靠,资源推荐率大于80%,资源推荐覆盖率广、推荐性强、实时性好、质量高,可以为用户提供及时的资源推荐服务,大大提高了教育资源的查找效率和资源利用率。
With the rapid development of education informatization,the development of network education resources has been highly valued at home and abroad. The information updating speed of all kinds of education resources is astonishing. In order to avoid waste of education resources,improve search efficiency,and timely recommend effective information materials for users,we design and implement a personalized recommendation system for educational resources by Java Web development technology in combination with network crawler technology and advance. The system uses Java crawler technology to obtain the information of public educational resources on specific educational resources websites as the data source of the system research,and uses matrix decomposition model,multi-layer perception model and NeuMF predictive analysis model to analyze and process user eigenvalue matrix and education resources eigenvalue matrix and correlate user information and educational resources information for user-education resources predictive value. The personalized recommendation technology of educational resources is studied. According to the priority of predictive value,the education resources information that meets the user needs is recommended to Web users. The system has been tested to be stable and reliable,with a resource recommendation rate of more than 80%. With wide coverage,strong recommendation,great real-time performance and high quality,it can provide users with timely resource recommendation services,greatly improving the search efficiency and resource utilization rate of educational resources.
With the rapid development of education informatization,the development of network education resources has been highly valued at home and abroad. The information updating speed of all kinds of education resources is astonishing. In order to avoid waste of education resources,improve search efficiency,and timely recommend effective information materials for users,we design and implement a personalized recommendation system for educational resources by Java Web development technology in combination with network crawler technology and advance. The system uses Java crawler technology to obtain the information of public educational resources on specific educational resources websites as the data source of the system research,and uses matrix decomposition model,multi-layer perception model and NeuMF predictive analysis model to analyze and process user eigenvalue matrix and education resources eigenvalue matrix and correlate user information and educational resources information for user-education resources predictive value. The personalized recommendation technology of educational resources is studied. According to the priority of predictive value,the education resources information that meets the user needs is recommended to Web users. The system has been tested to be stable and reliable,with a resource recommendation rate of more than 80%. With wide coverage,strong recommendation,great real-time performance and high quality,it can provide users with timely resource recommendation services,greatly improving the search efficiency and resource utilization rate of educational resources.
引文
[1] 李俊薇.基于协同过滤的校园教育资源网个性化推荐研究[D].武汉:华中师范大学,2009.
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[9] 赵建龙.基于协同过滤推荐技术的学习资源个性化推荐系统研究[D].杭州:浙江工业大学,2012.
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[12] AHER S B,LOBO L M R J.Combination of machine learning algorithms for recommendation of courses in e-learning system based on historical data[J].Knowledge-Based Systems,2013,51:1-14.
[13] 朱柳青.基于深度学习的课程推荐与学习预测模型研究[D].杭州:浙江工商大学,2018.
[14] 于湛麟,李仲秋,任永昌.SSH框架实现MVC架构的电子商务软件平台[J].计算机技术与发展,2012,22(10):169-172.
[15] 姜强,赵蔚,王朋娇,等.基于大数据的个性化自适应在线学习分析模型及实现[J].中国电化教育,2015(1):85-92.
[16] GARCíA E,ROMERO C,VENTURA S,et al.A collaborative educational association rule mining tool[J].The Internet and Higher Education,2011,14(2):77-88.
[2] 文勇军,吴冬冬,王键,等.Spark平台下教育资源个性化推荐研究[J].智能计算机与应用,2017,7(2):25-30.
[3] 方超,暴建民,薛四猛.基于领域特征值的协同过滤个性化推荐方法[J].计算机技术与发展,2017,27(11):88-91.
[4] 杨东衡.基于教育资源的推荐系统研究与实现[D].成都:电子科技大学,2017.
[5] SALEHI M,KAMALABADI I N,GHOUSHCHI M B G.An effective recommendation framework for personal learning environments using a learner preference tree and a GA[J].IEEE Transactions on Learning Technologies,2013,6(4):350-363.
[6] 丘少岖.基于知识点兴趣模型的教育资源个性化推荐系统[D].广州:华南理工大学,2016.
[7] 刘敏娜,赵蔷.SSI技术在教学资源平台应用的研究[J].计算机技术与发展,2017,27(9):170-174.
[8] 沈筱譞.在线学习系统中的深度学习推荐算法研究[D].武汉:华中师范大学,2017.
[9] 赵建龙.基于协同过滤推荐技术的学习资源个性化推荐系统研究[D].杭州:浙江工业大学,2012.
[10] 乔峰.基于模板化网络爬虫技术的Web网页信息抽取[D].成都:电子科技大学,2012.
[11] KLA?NJA-MILI■,et al.E-learning personalization based on hybrid recommendation strategy and learning style identification[J].Compu-ters & Education,2011,56(3):885-899.
[12] AHER S B,LOBO L M R J.Combination of machine learning algorithms for recommendation of courses in e-learning system based on historical data[J].Knowledge-Based Systems,2013,51:1-14.
[13] 朱柳青.基于深度学习的课程推荐与学习预测模型研究[D].杭州:浙江工商大学,2018.
[14] 于湛麟,李仲秋,任永昌.SSH框架实现MVC架构的电子商务软件平台[J].计算机技术与发展,2012,22(10):169-172.
[15] 姜强,赵蔚,王朋娇,等.基于大数据的个性化自适应在线学习分析模型及实现[J].中国电化教育,2015(1):85-92.
[16] GARCíA E,ROMERO C,VENTURA S,et al.A collaborative educational association rule mining tool[J].The Internet and Higher Education,2011,14(2):77-88.