期刊文献+

协同过滤推荐系统中数据稀疏问题的解决 预览 被引量:39

Algorithm for Sparse Problem in Collaborative Filtering
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摘要 介绍了现有协同过滤推荐的几种主要算法。它们对数据稀疏性问题都有一定的缓和够用。通过在数据集MovieLens上的实验,分析了各个算法在不同稀疏度下的推荐质量,为针对不同数据稀疏度的系统实现提供了可靠依据。 This paper summarized several primary algorithms, and experimented on MovieLens data. And analyzed different algorithm with the experimental results.
作者 吴颜 沈洁 顾天竺 陈晓红 李慧 张舒 WU Yan, SHEN Jie, GU Tian-zhu, CHEN Xiao-hong, LI Hui, ZHANG Shu, ( 1. Dept. of Computer Science, Institute of Information Technology, Yangzhou University, Yangzhou Jiangsu 225009, China ; 2. Dept. of Computer Science, Huaihai Institute of Technology, Lianyungang Jiangsu 222005, China)
出处 《计算机应用研究》 CSCD 北大核心 2007年第6期 94-97,共4页 Application Research of Computers
基金 江苏省自然科学基金资助项目(BK2005046)
关键词 电子商务 推荐系统 协同过滤 数据稀疏 相似性 e-commerce recommender system collaborative filtering data sparse similarity
作者简介 吴颜(1981-),陕西西安人,硕士研究生,研究方向为数据挖掘(wodename_wu@hotmail.com) 沈洁(1955-),男,江苏姜堰人,教授,硕导,研究方向为数据挖掘、信息管理 顾天竺(1981-),男,江苏无锡人,硕士研究生,研究方向为数据挖掘; 陈晓红(1981-),硕士研究生,研究方向为数据挖掘; 李慧(1979-),江苏连云港人,硕士研究生,研究方向为数据挖掘; 张舒(1979-),江苏连云港人,硕士研究生,研究方向为数据挖掘.
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参考文献17

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