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现代图书情报技术  2016, Vol. 32 Issue (4): 72-80    DOI: 10.11925/infotech.1003-3513.2016.04.09
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
一种利用用户学习树改进的协同过滤推荐方法
马莉()
天津外国语大学教育技术与实验室管理中心 天津 300204
Collaborative Filtering Recommendation Method Based on User Learning Tree
Ma Li()
Education Technology & Lab Management Center, Tianjin Foreign Studies University, Tianjin 300204, China
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摘要 

目的】利用学习树中知识点的属性和学习访问序列, 对知识点进行预测评分, 进而进行用户相似性聚类以实施协同过滤推荐, 改进传统在线学习推荐方法, 提高推荐质量。【方法】对用户所学知识点属性、知识点学习访问序列、学习频率、学习时间进行标准化处理构建学习树; 基于学习树, 对树中知识点进行预测评分; 基于预测评分和知识点属性、知识点学习序列分别利用Pearson相似性和余弦相似性进行用户相似性计算, 利用K均值聚类方法进行相似用户聚类, 进而利用协同过滤推荐方法进行在线学习推荐。【结果】通过F-measure指标进行实验评价, 结果表明该方法与传统在线学习协同过滤推荐方法相比, F-measure指标超过奇异值分解协同过滤8.22%, 超过平均分预测协同过滤3.75%。【局限】仅基于某在线学习平台的52 456条学生的学习记录和日志进行建模和测试, 未在其他数据集上进一步检验。【结论】解决了依赖用户评分进行协同过滤推荐的缺陷, 同时考虑了用户兴趣迁移对推荐准确率的影响, 对在线学习冷启动与可扩展性问题的解决具有较好的指导意义。

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马莉
关键词 在线学习推荐协同过滤学习树学习访问序列    
Abstract

[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 learner’s rate. The collaborative filtering recommendation was then carried out through similar learner clustring. [Methods] First, “attributes of resources”“resource access order” “learning frequency and time” were standardized to construct users’ learning tree and then predict resouces rating. Second, learner’s 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.

Key wordsE-Learning recommendation    Collaborative Filtering    Learning tree    Study access sequence
收稿日期: 2015-11-09     
引用本文:   
马莉. 一种利用用户学习树改进的协同过滤推荐方法[J]. 现代图书情报技术, 2016, 32(4): 72-80.
Ma Li. Collaborative Filtering Recommendation Method Based on User Learning Tree. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2016.04.09.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2016.04.09
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