摘要
针对协同过滤(CF)推荐方法用户的历史信息不足等问题,提出基于多分类器的迁移Bagging习题推荐算法。主要思路是把推荐问题投入迁移学习框架,将待推荐习题的用户作为目标域,从中搜索相似历史信息的用户作为辅助域,帮助训练目标域以得到更准确的分类结果。实验结果表明,所提方法在习题推荐库及公开数据上,比协同过滤算法性能提高了10%～20%;比单分类器Bagging迁移算法性能提升了5%～10%。该方法在一定程度上解决了习题推荐系统中存在的冷启动和数据稀疏问题,也可推广到商品推荐等电子商务平台。
Traditional Collaborative Filter （CF） often suffers from the shortage of historic information. A transfer-Bagging algorithm based on hybrid classifiers was proposed for question recommendation. The main idea was that the recommendation and prediction problem were cast into the framework of transfer learning, then the users＇ demand for recommend questions were treated as target domain, while similar users who had applicable historic information were employed as auxiliary domain to help training target classifiers. The experimental results on both question recommendation platform and popular open datasets show that the accuracy of the proposed algorithm is 10%-20% higher than CF, and 5%-10% higher than single Bagging algorithm. The method solves cold start-up and sparse data problem in question recommendation field, and can be generalized into production recommendation on E-commerce platform.
出处
《计算机应用》
CSCD
北大核心
2013年第7期1950-1954,共5页
journal of Computer Applications
基金
国家自然科学基金青年基金资助项目(61202184)
西北大学教改项目(ZC12020
JX12028)
校级创新基金资助项目(2011059)
关键词
迁移学习
BAGGING
协同过滤
推荐系统
计算机辅助教学
transfer learning Bagging Collaborative Filtering （CF） recommendation system Computer Assisted Instruction （CAI）
