一种利用用户学习树改进的协同过滤推荐方法

Objective] This paper aims to improve traditionlal recommendation method and quality of E-Learning enviroment, which used attributes and access orders of resources in learning tree to predict learners rate. The collaborative filtering recommendation was then carried out through similar learner clustring. [Methods] First, attributes of resourcesresource access order learning frequency and time were standardized to construct users learning tree and then predict resouces rating. Second, learners similarity was calculated through Pearson and Cosine function respectivly based on predicted ratings. Third, K-means clustering method was used to group similar learners to establish collaborative filteing system for online E-learing. [Results] Compared with traditional collaborative filtering method, F-measure experimental result of the proposed method was 8.22% higher than the singular value decomposition CF and was 3.75% higher than the average score forecast CF. [Limitations] The proposed method was only tested on the dataset from one online learing platform with 52,456 students learning records and access logs. More research is needed to examine the method with other data sets. [Conclusions] The proposed collaborative filtering recommendation system does not rely on learners ratings and considers the influence of learners interest changes. It could help us deal with the starting and expanding issues.