基于协同过滤的高校移动通信课程教学资源个性化推荐方法  

Personalized recommendation method of teaching resources of college mobile communication courses based on collaborative filtering

作　　者：刘磊[1] LIU Lei(Chengdu College,University of Electronic Scienceand Technology,Chengdu 611731,China)

机构地区：[1]电子科技大学成都学院,四川成都611731

出　　处：《无线互联科技》2025年第4期92-95,共4页Wireless Internet Science and Technology

基　　金：四川省教育厅高等教育人才培养和教学改革重大项目,项目名称:融汇“工程思政”的应用型高校电子信息类专业产教融合协同育人体系的研究与实践,项目编号:JG2023-P17。

摘　　要：由于教学资源规模庞大且种类繁多,传统推荐系统难以捕捉到用户深层次的个性化需求,为此,文章提出一种基于协同过滤的高校移动通信课程教学资源个性化推荐方法。以学生的学习行为数据和历史记录为基础,深入分析学生在兴趣偏好、学习能力、学习进度3个维度的个性化特征;采用协同过滤算法,通过计算用户间的余弦相似度,识别与目标用户兴趣、学习习惯相似的学生群体;根据相似学生的资源偏好,对候选资源进行加权评分,按照评分高低进行排序,生成个性化的推荐列表。实验结果显示,该方法在命中率(Hits Ratio,HR)和归一化折扣累积增益(Normalized Discounted Cumulative Gain,NDCG)评估指标上均表现出色,说明该方法不仅在推荐结果的可靠性方面具有显著优势,而且在提升用户体验和学习效率方面表现出更高的有效性。Due to the large scale and diverse types of teaching resources,traditional recommendation systems are difficult to capture users’deep level personalized needs.Therefore,a personalized recommendation method for mobile communication course teaching resources in universities based on collaborative filtering is proposed.Based on students’learning behavior data and historical records,conduct in-depth analysis of their personalized characteristics in three dimensions:interest preferences,learning abilities,and learning progress.The article uses collaborative filtering algorithm,by calculating the cosine similarity between users,identify student groups with similar interests and learning habits to the target user.Based on the resource preferences of similar students,the candidate resources are weighted and ranked according to their scores to generate a personalized recommendation list.The experimental results show that the designed recommendation method performs well in both Hits Ratio(HR)and Normalized Discounted Cumulative Gain(NDCG)evaluation metrics,indicating that this method not only has significant advantages in the reliability of recommendation results,but also shows higher effectiveness in improving user experience and learning efficiency.

关 键 词：协同过滤 移动通信课程 教学资源 个性化推荐 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]