Guess you like: course recommendation in MOOCs

Xia Jing, Jie Tang
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引用次数: 65

Abstract

Recommending courses to online students is a fundamental and also challenging issue in MOOCs. Not exactly like recommendation in traditional online systems, students who enrolled the same course may have very different purposes and with very different backgrounds. For example, one may want to study "data mining" after studying the course of "big data analytics" because the former is a prerequisite course of the latter, while some other may choose "data mining" simply because of curiosity. Employing the complete data from XuetangX1, one of the largest MOOCs in China, we conduct a systematic investigation on the problem of student behavior modeling for course recommendation. We design a content-aware algorithm framework using content based users' access behaviors to extract user-specific latent information to represent students' interest profile. We also leverage the demographics and course prerequisite relation to better reveal users' potential choice. Finally, we develop a course recommendation algorithm based on the user interest, demographic profiles and course prerequisite relation using collaborative filtering strategy. Experiment results demonstrate that the proposed algorithm performs much better than several baselines (over 2X by MRR). We have deployed the recommendation algorithm onto the platform XuetangX as a new feature, which significantly helps improve the course recommendation performance (+24.6% by click rate) comparing with the recommendation strategy previously used in the system.
我猜你喜欢:mooc课程推荐
向在线学生推荐课程是mooc的一个基本问题,也是一个具有挑战性的问题。与传统的在线推荐系统不完全一样,选修同一门课程的学生可能有非常不同的目的和背景。例如,有人可能在学习了“大数据分析”课程后,又想学习“数据挖掘”,因为前者是后者的必修课程,而另一些人可能只是出于好奇而选择“数据挖掘”。我们利用国内最大的mooc之一学堂x1的完整数据,对学生行为建模在课程推荐中的问题进行了系统的研究。我们设计了一个内容感知算法框架,利用基于内容的用户访问行为来提取用户特定的潜在信息来代表学生的兴趣概况。我们还利用人口统计和课程先决条件的关系来更好地揭示用户的潜在选择。最后,采用协同过滤策略,基于用户兴趣、人口统计资料和课程先决条件关系,开发了一种课程推荐算法。实验结果表明,该算法的性能优于几种基准(MRR大于2X)。我们将推荐算法作为一个新特性部署到XuetangX平台上,与系统之前使用的推荐策略相比,显著提高了课程推荐性能(点击率+24.6%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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