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PERSONALIZED TEACHING RESOURCE RECOMMENDATION METHOD FOR LARGE-SCALE USERS

机译:大型用户的个性化教学资源推荐方法

摘要

Provided is a personalized teaching resource recommendation method for large-scale users, comprising: obtaining user interaction data, and performing data pre-processing of the user interaction data to obtain a user resource scoring matrix; performing feature dimensionality reduction on the user resource scoring matrix to obtain a user's teaching resource feature matrix; clustering the teaching resource feature matrix to obtain clusters of teaching resources, and sorting the teaching resources in the teaching resource clusters; obtaining user ratings of all teaching resources, and using a teaching resource interest model in sequence to calculate the user's degree of interest in the teaching resources, and arranging all teaching resources in descending order according to the degree of interest to generate a list of recommended teaching resources. The method can provide a large number of users with fast and accurate digital teaching resource recommendation services, thus enhancing user experience, and provide a set of effective solutions for the personalized utilization of teaching resources at a smart campus.
机译:提供了大型用户的个性化教学资源推荐方法,包括:获取用户交互数据,并执行用户交互数据的数据预处理,以获得用户资源评分矩阵;对用户资源评分矩阵进行特征维度减少以获得用户的教学资源特征矩阵;聚类教学资源特征矩阵获取教学资源集群,并在教学资源集群中对教学资源进行分类;获取所有教学资源的用户评级,并使用教学资源利益模型顺序计算用户对教学资源的兴趣程度,并根据兴趣程度将所有教学资源按降序排列,以产生推荐教学列表。资源。该方法可以为大量用户提供快速和准确的数字教学资源推荐服务,从而提高用户体验,并为智能校园提供了一组有效的教学资源利用的有效解决方案。

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