基于数据挖掘的在线学习平台个性化推荐系统设计

Design of Personalized Recommendation System for Online Learning Platform Based on Data Mining

本文结合数据挖掘技术,设计并实现了一个个性化推荐系统。系统首先根据用户的学习行为以及动态特征进行分析,并对每种特征设置一定的权重完成学习需求挖掘。然后将学习者的兴趣向量与学习资源特征向量的相似度进行匹配,判断后列出最优推荐资源。结果显示,通过本文的个性化推荐系统进行在线学习之后,有65%的学生成绩提高了,且系统的精度和召回率分别达到了0.40与0.20,可以在一定程度上帮助用户进行个性化高效学习。

In this paper,a personalized recommendation system is designed and implemented by combining data mining technology.The system first analyzes the user's learning behavior and dynamic features,and sets certain weights for each feature to complete the learning demand mining.Then it matches the similarity between the learner's interest vector and the learning resource feature vector,and lists the optimal recommended resources after judgment.The results show that 65%of the students'grades are improved after online learning through the personalized recommendation system in this paper.And the precision and recall of the system reached 0.40 and 0.20 respectively,which can help users to personalize and efficient learning to a certain extent.