基于协同过渡算法的学习资源个性化推荐系统设计与实现

发布时间：2018-05-15 12:17

本文选题：学习资源 + 协同过滤　；参考：《天津师范大学》2017年硕士论文

【摘要】：当前,互联网技术与现代远程教育技术正处于高速融合期,以学习者为中心的网络学习以其不受时空限制、费用成本低、服务功能完善等优势迅速成为现代教育教学体系中重要的形式之一。然而,学习者在海量学习资源中却面临着无从选择的境地,这就是人们所说的"信息过载"问题。"信息过载"问题一直困扰着学习者,如何解决它迫在眉睫。借鉴个性化推荐系统在音乐、视频、新闻媒体、电子商务等的成功应用,教育领域的研究学者也开始将目光从传统教学转向互联网环境下的混合式教学。本课题研究了互联网中常见的几个学习平台,总结在个性化推荐服务中存在的一些不足。针对这些不足,以天津师范大学的大学生群体为研究对象,设计并开发学习资源个性化推荐系统。结合学习资源这一特定的推荐对象对当前主流的基于内容的过滤算法、基于关联规则的推荐算法和协同过滤算法进行分析,确定采用协同过滤算法来优化推送信息。同时,考虑当前互联网学习者的跨平台学习情况,设计开发的学习资源个性化推荐系统将支持PC、手机、PAD。
[Abstract]:At present, Internet technology and modern distance education technology are in a period of high speed integration. The learner-centered network learning is not limited by time and space, and the cost is low. The service function consummation and so on superiority quickly becomes one of the important forms in the modern education and teaching system. However, learners are faced with a situation in which there is no choice in the massive learning resources. This is what people call "information overload". The problem of information overload has been puzzling learners, how to solve it is urgent. Learning from the successful application of personalized recommendation system in music, video, news media, electronic commerce and so on, scholars in the field of education have begun to shift their attention from traditional teaching to hybrid teaching under the environment of Internet. This paper studies several common learning platforms in the Internet and summarizes some shortcomings in personalized recommendation services. Aiming at these shortcomings, this paper designs and develops a personalized recommendation system for learning resources, taking the college students of Tianjin normal University as the research object. This paper analyzes the current mainstream content-based filtering algorithm, association rule-based recommendation algorithm and collaborative filtering algorithm combined with learning resources as a specific recommendation object, and determines that collaborative filtering algorithm is used to optimize push information. At the same time, considering the cross-platform learning situation of Internet learners, the personalized recommendation system of learning resources designed and developed will support PC-PAD.
【学位授予单位】：天津师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：G434

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