基于多分类器的迁移Bagging习题推荐

doi:10.11772/j.issn.1001-9081.2013.07.1950

计算机应用 ›› 2013, Vol. 33 ›› Issue (07): 1950-1954.DOI: 10.11772/j.issn.1001-9081.2013.07.1950

基于多分类器的迁移Bagging习题推荐

吴云峰,冯筠,孙霞,李展,冯宏伟,贺小伟

西北大学信息科学与技术学院，西安 710069

收稿日期:2013-01-16 修回日期:2013-03-08 出版日期:2013-07-01 发布日期:2013-07-06
通讯作者: 冯筠
作者简介:吴云峰(1988-),男，江西赣州人，硕士研究生,主要研究方向：个性化推荐、模式识别；冯筠(1972-),女,陕西西安人,教授,博士生导师,主要研究方向：模式识别、医学图像处理、个性化教学；孙霞(1977-),女,陕西西安人,副教授,主要研究方向：文本挖掘、信息检索。
基金资助:
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)

Online transfer-Bagging question recommendation based on hybrid classifiers

WU Yunfeng,FENG Jun,SUN Xia,LI Zhan,FENG Hongwei,HE Xiaowei

School of Information Science and Technology, Northwest University, Xi'an Shaanxi 710069, China

Received:2013-01-16 Revised:2013-03-08 Online:2013-07-06 Published:2013-07-01
Contact: FENG Jun

摘要/Abstract

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。

关键词: 迁移学习, Bagging, 协同过滤, 推荐系统, 计算机辅助教学

Abstract: 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.

Key words: transfer learning, Bagging, Collaborative Filtering (CF), recommendation system, Computer Assisted Instruction (CAI)

中图分类号:

TP391.7

吴云峰冯筠孙霞李展冯宏伟贺小伟. 基于多分类器的迁移Bagging习题推荐[J]. 计算机应用, 2013, 33(07): 1950-1954.

WU Yunfeng FENG Jun SUN Xia LI Zhan FENG Hongwei HE Xiaowei. Online transfer-Bagging question recommendation based on hybrid classifiers[J]. Journal of Computer Applications, 2013, 33(07): 1950-1954.

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基于多分类器的迁移Bagging习题推荐

Online transfer-Bagging question recommendation based on hybrid classifiers

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