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现代图书情报技术  2014, Vol. 30 Issue (6): 25-32    DOI: 10.11925/infotech.1003-3513.2014.06.04
  知识组织与知识管理 本期目录 | 过刊浏览 | 高级检索 |
一种面向用户偏好定向挖掘的协同过滤个性化推荐算法
王伟军, 宋梅青
华中师范大学信息管理学院 武汉 430079;
华中师范大学青少年网络心理与行为教育部重点实验室 武汉 430079
A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining Users’ Preferences
Wang Weijun, Song Meiqing
School of Information Management, Central China Normal University, Wuhan 430079, China;
Key Laboratory of Adolescent Cyberpsychology and Behavior, Ministry of Education, Central China Normal University, Wuhan 430079, China
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摘要 

[目的]解决协同过滤推荐的可扩展性问题和数据稀疏性问题。[方法]提出一种面向用户偏好定向挖掘的协同过滤算法。该算法以时间为约束, 第一阶段先寻找基于项目的弱相似用户; 第二阶段基于用户关联性和属性相似性进行定向挖掘, 形成推荐集合。[结果]实验结果表明, 新算法的时间复杂度降低一个数量级, 并且数据越稀疏, 推荐精度的领先优势越大。[局限]该算法基于用户已表现出的偏好进行深度推荐, 对未表现出的其他偏好暂未涉及。[结论]该算法在提升可扩展性的同时, 对数据稀疏性也有很强的适应能力。

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关键词 协同过滤用户偏好个性化推荐推荐算法    
Abstract

[Objective] To solve the scalability problem and data sparsity problem of the collaborative filtering. [Methods]This paper proposes an algorithm of collaborative filtering personalized recommendation through directionally mining users' preferences. Introducing time as a variable, the algorithm excavates in two stages. The first stage is to find the project-based weak similar users, the second stage is to use users' relevance and attribute similarity so as to do directional excavation and form a collection of recommendation. [Results]Experimental results show that the time complexity of the new algorithm reduces a magnitude. Furthermore, the more sparser the data is, the greater leading advantage the recommendation accuracy has. [Limitations] The algorithm recommends deeply by analyzing the users' existed preferences, and it doesn't involve the users' preferences which haven't appeared. [Conclusions]This algorithm has a strong ability to adapt to data sparsity and enhances its scalability at the same time.

Key wordsCollaborative filtering    User preferences    Personalized recommendation    Recommendation algorithm
收稿日期: 2013-12-23     
:  G202  
基金资助:

本文系国家自然科学基金项目“基于用户偏好感知的SaaS 服务选择优化研究”(项目编号: 71271099)和湖北省自然科学基金创新群体重点项目“基于云计算的知识集成与服务研究”(项目编号: 2011CDA116)的研究成果之一。

通讯作者: 宋梅青E-mail:mqsong99@126.com     E-mail: mqsong99@126.com
作者简介: 作者贡献声明:王伟军:确定研究方向及研究方法,提出论文的修订意见;宋梅青:进行算法设计及实验分析,负责论文的撰写与修订。
引用本文:   
王伟军, 宋梅青. 一种面向用户偏好定向挖掘的协同过滤个性化推荐算法[J]. 现代图书情报技术, 2014, 30(6): 25-32.
Wang Weijun, Song Meiqing. A Collaborative Filtering Personalized Recommendation Algorithm Through Directionally Mining Users’ Preferences. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2014.06.04.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2014.06.04

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