【摘要】 个性化推荐是用户从海量信息中选取有用信息的有效途径。用户兴趣作为社会化标签被抽取出来表示用户特征,作为个性化推荐的重要参考依据。本文以在线学习为应用背景,通过抽取用户对知识点的掌握程度作为用户特征进行关联分析,构建用户兴趣模型和学习能力评估模型,并在此基础上,借鉴协同过滤算法的思想,架构了基于用户兴趣模型和学习能力评估模型的个性化学习系统框架,以期为在线学习的个性化信息服务的优化和实施提供理论与应用参考。更多还原

【Abstract】 Personalized recommendation is an effective way for users to select useful information from mass information. User interest is extracted as a social label to represent user characteristics as an important reference for personalized recommendation. Taking online learning as the application background, this paper constructs the user interest model and learning ability evaluation model by extracting the user mastery of knowledge points as the user characteristics to carry on the correlation analysis. And on this basis,the framework of personalized learning system based on user interest model and learning ability evaluation model is constructed by using the idea of collaborative filtering algorithm, which provides theoretical and practical reference for the optimization and implementation of personalized information service for online learning.更多还原