The current personalized recommendation methods for teaching resources of university courses suffer from poor recommendation effectiveness due to the absence of user tags. To address this issue, a new personalized recommendation method based on cluster analysis is proposed. The proposed method leverages web crawler technology to obtain user tags, followed by processing the tags to remove meaningless terms, normalize word forms, and perform data processing. The processed tags are used to calculate user interest preferences for each tag cluster generated by clustering. Based on this, a user interest model is built, and user similarity is calculated to determine the recommendation score of each resource. The recommended resources are then ranked according to their recommendation score and presented to the target user. Experimental results demonstrate that the proposed method achieves high accuracy, recall rate, and F1 value for personalized recommendation of teaching resources in colleges and universities. In comparison, the method proposed in this paper has a significantly shorter recommendation time of 10.65 s. Further, the proposed model not only takes less time but also has higher recommendation efficiency when compared with existing personalized recommendation methods.

中文翻译：

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聚类分析及其在大学课程教学资源中的应用：一种个性化方法

目前针对大学课程教学资源的个性化推荐方法，由于缺乏用户标签，推荐效果较差。针对这一问题，提出了一种基于聚类分析的个性化推荐方法。该方法利用网络爬虫技术获取用户标签，然后对标签进行处理以去除无意义的术语，规范词形并进行数据处理。处理后的标签用于计算聚类生成的每个标签簇的用户兴趣偏好。在此基础上，建立用户兴趣模型，计算用户相似度，确定各个资源的推荐分数。然后根据推荐分数对推荐资源进行排序并呈现给目标用户。实验结果表明，该方法在高校教学资源个性化推荐中具有较高的准确率、召回率和F1值。相比之下，本文提出的方法的推荐时间明显缩短为10.65 s。此外，与现有的个性化推荐方法相比，所提出的模型不仅花费更少的时间，而且具有更高的推荐效率。