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现代图书情报技术  2015, Vol. 31 Issue (12): 28-33    DOI: 10.11925/infotech.1003-3513.2015.12.05
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
面向协同过滤推荐的多粒度用户偏好挖掘研究
宋梅青
武汉大学信息管理学院 武汉 430072
Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation
Song Meiqing
School of Information Management, Wuhan University, Wuhan 430072, China
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摘要 

[目的]针对协同过滤中用户偏好挖掘粒度与挖掘效率之间的关系展开研究, 以期找出效率最高的挖掘粒度。[方法]结合实际应用情况将用户偏好挖掘粒度从粗到细划分为三种, 并对三种粒度下相应的偏好挖掘算法进行详细设计, 通过实验对比不同粒度下用户偏好挖掘的效率。[结果]实验结果表明, 当用户偏好挖掘粒度从粗到细变化时, 偏好挖掘效率也会逐渐降低。[局限]以用户消费及评分数据为挖掘用户偏好的数据来源, 对于其他类型数据源暂未涉及。[结论]粗粒度的偏好挖掘能更好地发现用户偏好。

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Abstract

[Objective] Researching the relationship between users' preference mining granularity and mining efficiency in collaborative filtering, this paper aims at finding out the most efficient mining granularity. [Methods] According to the practical application, the users' preference mining granularity is divided into three kinds from coarse-grained to fine-grained, and then design the corresponding preference mining algorithm under the three kinds of granularities, finally contrast users' preference mining efficiency under different granularities through experiments. [Results] Experimental results show that the preference mining efficiency reduces as the users' preference mining granularity changes from coarse to fine. [Limitations] Data only includes users' consumption data and rating data, other types of data are not covered temporarily. [Conclusions] Coarse-grained preference mining is better for discovering users' preferences.

收稿日期: 2015-06-05     
:  G202  
通讯作者: 宋梅青, ORCID: 0000-0002-1447-3883, E-mail: mqsong99@126.com。     E-mail: mqsong99@126.com
引用本文:   
宋梅青. 面向协同过滤推荐的多粒度用户偏好挖掘研究[J]. 现代图书情报技术, 2015, 31(12): 28-33.
Song Meiqing. Research on Multi-granularity Users' Preference Mining Based on Collaborative Filtering Personalized Recommendation. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.12.05.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.12.05

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