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现代图书情报技术  2010, Vol. 26 Issue (11): 37-41    DOI: 10.11925/infotech.1003-3513.2010.11.06
  知识组织与知识管理 本期目录 | 过刊浏览 | 高级检索 |
电子商务协同过滤可扩展性研究综述
李聪
四川师范大学计算机科学学院 成都 610066
Review of Scalability Problem in E-commerce Collaborative Filtering
Li Cong
School of Computer Science,Sichuan Normal University, Chengdu 610066, China
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摘要 

在介绍传统协同过滤算法的基础上,将协同过滤可扩展性改善技术归纳为6类,包括聚类、概率方法、降维、基于项目、数据集缩减以及线性模 型,重点评述各类算法的研究情况,并将其基本思路总结为两点:在尽量不影响推荐质量的前提下,缩小最近邻查询空间;定期离线进行用户相似性度量和最近邻搜寻,减小在线推荐计算量。最后探讨该领域未来的两个研究方向,即基于分布式结构的协同过滤算法、基于形式概念分析的最近邻搜寻。

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李聪
关键词 电子商务推荐系统协同过滤可扩展性    
Abstract

Based on the introduction of basic collaborative filtering algorithm, six kinds of techniques which are used to ameliorate the scalability problem are generalized, including clustering, probabilistic approach, dimensionality reduction, item-based, dataset reduction and linear model. The collaborative filtering algorithms with aforementioned techniques are commented emphatically, and their ideas are summarized in two points: reducing the neighborhood search space under the precondition of unaffected recommendation quality; periodically running user similarity measuring and neighborhood research offline to reduce the recommendation computation online. Two future research directions on the scalability problem in collaborative filtering are discussed finally, namely the collaborative filtering algorithm based on distributed structure, and the neighborhood search based on formal concept analysis.

Key wordsE-commerce    Recommender systems    Collaborative filtering    Scalability
收稿日期: 2010-09-29     
: 

C931

 
基金资助:

本文系四川省教育厅青年基金项目“电子商务协同过滤推荐稀疏性问题研究”(项目编号: 09ZB068)的研究成果之一。

引用本文:   
李聪. 电子商务协同过滤可扩展性研究综述[J]. 现代图书情报技术, 2010, 26(11): 37-41.
Li Cong. Review of Scalability Problem in E-commerce Collaborative Filtering. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2010.11.06.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2010.11.06


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