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现代图书情报技术  2015, Vol. 31 Issue (1): 45-51    DOI: 10.11925/infotech.1003-3513.2015.01.07
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于粗糙用户聚类的协同过滤推荐模型
王晓耘, 钱璐, 黄时友
杭州电子科技大学管理学院 杭州 310012
Collaborative Filtering Recommendation Model Based on Rough User Clustering
Wang Xiaoyun, Qian Lu, Huang Shiyou
Management School, Hangzhou Dianzi University, Hangzhou 310012, China
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摘要 

[目的] 将粗糙集引入到基于用户聚类的协同过滤中, 提高推荐质量。[方法] 提出一种基于粗糙用户聚类的协同过滤推荐模型: 离线时采用粗糙K-means用户聚类算法, 根据用户与聚类中心的相似度将其分配到K个类的上、下近似中, 形成用户的初始近邻集; 在线时从目标用户的初始近邻集中搜索其最近邻, 预测项目评分并向其产生推荐。[结果] 通过实验对比发现,该模型比传统的和基于项目的协同过滤推荐算法降低约14%的平均绝对误差, 比基于用户聚类的协同过滤推荐算法降低约10%的平均误差。[局限] 在考虑上、下近似对聚类中心调整的重要程度时, 忽略了用户聚类数目和最近邻集用户数阈值的变化所产生的影响。[结论] 该模型能有效提高推荐精度, 具有较强的可行性和现实意义。

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关键词 粗糙集用户聚类协同过滤上下近似    
Abstract

[Objective] In order to improve the quality of recommendation, rough set is introduced into collaborative filtering based on user clustering. [Methods] This paper proposes a collaborative filtering recommendation model based on rough user clustering. When off-line, it clusters all users by rough K-means user clustering algorithm, which assigns user to upper or lower approximation based on similarity and thus generates his initial neighbor. When on-line, the model starts searching the nearest neighbor from the target user's initial neighbor, forecasts his ratings and makes recommendation. [Results] Experimental results show that the proposed model decreases the Mean Absolute Error (MAE) about 14% when compared with traditional and item-based collaborative filtering, and decreases MAE about 10% when compared with collaborative filtering based on user clustering. [Limitations] When considering the importance of upper and lower approximation to adjusting the centroid of cluster, this paper ignores the impact of the number of user clusters and the threshold of the number of nearest neighbors. [Conclusions] This model can effectively improve recommendation accuracy, and has high feasibility and practical significance.

Key wordsRough set    User clustering    Collaborative filtering    Upper or lower approximation
收稿日期: 2014-07-02     
:  G254  
  TP391  
基金资助:

本文系杭州电子科技大学研究生科研创新基金项目"基于粗糙集的协同过滤推荐算法改进及应用"(项目编号:KYCX2013JJ028)的研究成果之一。

通讯作者: 钱璐,ORCID:0000-0001-6025-3665,E-mail:15158113182@163.com。     E-mail: 15158113182@163.com
作者简介: 作者贡献声明: 王晓耘, 钱璐: 提出研究思路, 设计研究方案; 黄时友: 采集和分析数据, 进行实验; 钱璐: 论文起草; 王晓耘: 论文最终版本修订。
引用本文:   
王晓耘, 钱璐, 黄时友. 基于粗糙用户聚类的协同过滤推荐模型[J]. 现代图书情报技术, 2015, 31(1): 45-51.
Wang Xiaoyun, Qian Lu, Huang Shiyou. Collaborative Filtering Recommendation Model Based on Rough User Clustering. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.01.07.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.01.07

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