基于二部图的学习资源混合推荐方法研究  

Research on Mixed Recommendation Method of Learning Resources Based on Bipartite Network

作　　者：刘忠宝[1] 李花 宋文爱[1] 孔祥艳 李宏艳[1] 张静[1] LIU Zhongbao;LI Hua;SONG Wenai;KONG Xiangyan;LI Hongyan;ZHANG Jing(School of Software,North University of China,Taiyuan Shanxi 030051)

机构地区：[1]中北大学软件学院,山西太原030051

出　　处：《电化教育研究》2018年第8期85-90,共6页E-education Research

基　　金：国家社会科学基金教育学青年课题"云环境下基于兴趣图谱的个性化学习资源推荐方法研究"(课题编号:CCA150161)

摘　　要：在线学习过程中,学习者经常面临"资源过载"和"学习迷航"问题。为了解决上述问题,研究人员将信息推荐技术引入在线学习中,试图为学习者提供个性化的学习服务。近年来,协同过滤推荐方法受到广泛关注,并在实际应用中取得了较为理想的效果。然而,该方法推荐范围有限且忽视小众资源,无法满足部分学习者的个性化需求。鉴于此,文章将物理学中的热传导和物质扩散理论引入推荐系统中,建立基于二部图的学习资源混合推荐模型。该模型将基于热传导的推荐方法和基于物质扩散的推荐方法混合使用,通过引入一个可调参数,使两种方法在不同应用场景下发挥不同的作用。该模型为进一步研究个性化学习资源推荐方法提供重要参考。Learners often encounter some problems in online learning, such as ＂resource overload＂ and ＂learning loses＂. In order to solve the above problems, information recommendation technology has been introduced into online learning, so as to provide learners with personalized learning services. In recent years, the collaborative filtering recommendation method has received wide attention and achieved satisfactory results in practical application. However, by using this method, the range of recommendations is limited and niche resources are ignored, which cannot meet learners＇ individual needs. Therefore, this paper introduces the theory of heat conduction and material diffusion in physics into recommendation system, and establishes a mixed recommendation model of learning resources based on bipartite network.The model will use recommendation methods based on both heat conduction and material diffusion, and the two methods can play different roles in different application scenarios according to an adjustable parameter. This model provides an important reference for further research on resource recommendation of personalized learning.

关 键 词：学习资源推荐 二部图 热传导 物质扩散 混合推荐 

分 类 号：G434[文化科学—教育学]