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现代图书情报技术  2015, Vol. 31 Issue (6): 13-19    DOI: 10.11925/infotech.1003-3513.2015.06.03
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于用户分类的协同过滤个性化推荐方法研究
祝婷, 秦春秀, 李祖海
西安电子科技大学经济与管理学院 西安 710071
Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification
Zhu Ting, Qin Chunxiu, Li Zuhai
School of Economics and Management, Xidian University, Xi'an 710071, China
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摘要 

目的】解决随着用户数目剧增而造成的协同过滤算法效率过低的问题。【方法】提出一种基于用户分类的协同过滤方法。该方法引入基于规则的分类方法对庞大的用户群分类, 在保证一定的推荐准确度前提下, 为用户寻找局部近邻用户, 并以局部近邻用户基准完成个性化推荐。【结果】分别通过F1与平均绝对误差两个指标进行用户分类与推荐精度评估, 在用户分类准确及推荐精度良好的前提下, 用时间复杂度衡量算法效率。实验结果表明, 引入用户分类的协同过滤推荐效率明显提高。【局限】牺牲一定程度的推荐精度; 仅在MovieLens公开数据集上进行实验测试, 还需在其他数据集上进一步检验。【结论】本文方法可以减少近邻用户识别的计算量, 同时提高算法效率。

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关键词 个性化推荐协同过滤用户分类规则    
Abstract

[Objective] To solve the problem of low efficiency of the algorithm with the increasing number of users. [Methods] This paper proposes a method of collaborative filtering based on user classification. Firstly, the huge users are classified into several groups according to a rule-based classification method. Then, with the guarantee of recommendation accuracy, the local neighbor users are discovered for users. Finally, based on the discovered local neighbors, personalized recommendation is conducted. [Results] User classification and recommendation accuracy are evaluated by F1 and MAE separately. The algorithm efficiency is evaluated according to the time complexity. Experimental results show that with the adoption of a rule-based user classification, collaborative filtering algorithm significantly improves with the guarantee of user classification accuracy and recommendation accuracy. [Limitations] The recommendation accuracy is reduced a little bit. The proposed method is only tested on MovieLens data set, and it needs further validation in other data sets. [Conclusions] This method reduces the computation of local neighbors user identification, while improves the efficiency of the algorithm.

Key wordsPersonalized recommendation    Collaborative filtering    User classification    Rule
收稿日期: 2014-12-31     
:  G350  
基金资助:

本文系国家自然科学基金项目“基于知识地图的对等网语义社区及其知识共享研究”(项目编号:71103138)和中央高校基本科研业务费专项资金资助项目“大数据背景下基于用户生成内容的商务智能模型研究”(项目编号: BDY231414)的研究成果之一。

通讯作者: 秦春秀, ORCID: 0000-0002-7809-4145, E-mail: cxqin@xidian.edu.cn。     E-mail: cxqin@xidian.edu.cn
作者简介: 作者贡献声明: 祝婷: 提出研究思路, 设计研究方案, 采集、清洗和分析数据; 李祖海: 进行实验; 祝婷, 秦春秀: 论文起草及最终版本修订。
引用本文:   
祝婷, 秦春秀, 李祖海. 基于用户分类的协同过滤个性化推荐方法研究[J]. 现代图书情报技术, 2015, 31(6): 13-19.
Zhu Ting, Qin Chunxiu, Li Zuhai. Research on Collaborative Filtering Personalized Recommendation Method Based on User Classification. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.06.03.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.06.03

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