首页 | 本学科首页   官方微博 | 高级检索  
     检索      

基于两阶段聚类的协作过滤推荐算法
引用本文:龚瑞君,王佳,戴珺,李自力,李庆.基于两阶段聚类的协作过滤推荐算法[J].郑州大学学报(理学版),2010,42(1).
作者姓名:龚瑞君  王佳  戴珺  李自力  李庆
作者单位:西南财经大学,经济信息工程学院,四川,成都,610074
基金项目:国家自然科学基金资助项目,编号60803106
摘    要:协作过滤推荐是目前主流的个性化推荐方式,但数据稀疏问题影响了推荐系统的性能.提出了基于两阶段聚类的协作推荐算法,降低了数据的稀疏性,提高了最近邻的准确度,而且推荐精度较以往传统的算法有明显提高,时间复杂度也有明显降低.

关 键 词:稀疏性  聚类  协作过滤

Collaborative Filtering Algorithm Based on Two-phase Clustering
GONG Rui-jun , WANG Jia , DAI Jun , LI Zi-li , LI Qing.Collaborative Filtering Algorithm Based on Two-phase Clustering[J].Journal of Zhengzhou University:Natural Science Edition,2010,42(1).
Authors:GONG Rui-jun  WANG Jia  DAI Jun  LI Zi-li  LI Qing
Institution:GONG Rui-jun,WANG Jia,DAI Jun,LI Zi-li,LI Qing(School of Economic Information Engineering,Southwest University of Finance , Economics,Chengdu 610074,China)
Abstract:Collaborative filtering recommendation is more successful personalized recommendation algorithm.However,the data sparsity decreases the performance of recommendation.The two-phase clustering-based collaborative filtering algorithm is proposed.The algorithm not only reduces the sparsity of data,but also improves the accuracy of the nearest neighbor and the recommendation accuracy.And it reduces the time complexity.
Keywords:sparsity  clustering  collaborative filtering  
本文献已被 CNKI 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号