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现代图书情报技术  2015, Vol. 31 Issue (11): 12-17    DOI: 10.11925/infotech.1003-3513.2015.11.03
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于用户兴趣模糊聚类的协同过滤算法
刘占兵1, 肖诗斌1,2
1 北京信息科技大学计算机学院 北京 100101;
2 北京拓尔思信息技术股份有限公司 北京 100101
Collaborative Filtering Recommended Algorithm Based on User's Interest Fuzzy Clustering
Liu Zhanbing1, Xiao Shibin1,2
1 Computer School, Beijing Information Science and Technology University, Beijing 100101, China;
2 Beijing TRS Information Technology Co., Ltd., Beijing 100101, China
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摘要 

[目的]解决传统协同过滤推荐算法存在的数据稀疏性、用户不同时间的兴趣被等同考虑的问题。[方法]提出一种基于用户兴趣模糊聚类的协同过滤算法。将用户兴趣模型分为稳定兴趣和当前兴趣, 利用用户稳定兴趣对用户进行模糊聚类, 确定用户最近邻, 形成初始推荐集; 计算推荐列表中各个项目和用户当前兴趣的相似度, 然后按照相似度大小排序, 生成最终推荐列表。[结果]在数据集MovieLens上验证本方法的推荐准确率, 其平均绝对误差(MAE)较传统方法降低近10%。[局限]该算法中, 在对用户稳定兴趣建模时考虑所有的项目类别, 没有对项目类别进行处理(如合并和删除等)。[结论]与传统的推荐算法相比, 该方法的推荐准确度有明显提高。

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Abstract

[Objective] Solve the problems in the traditional collaborative filtering recommendation algorithm, such as sparse data and user's interests in different time being considered equally.[Methods] This paper proposes a collaborative filtering algorithm based on user's interest fuzzy clustering. In the algorithm, the model of user's interest consists of the stable interest and the current interest. Users are clustered by the fuzzy clustering according to the stable interest, then the nearest neighbours and the initial recommendation list can be obtained. The final recommendation list is generated by sorting the similarity between the each item of initial recommendation list and user current interest, on the basis of the initial recommendations. [Results] The Mean Absolute Error (MAE) of the proposed method is nearly 10% reduction verified on the MovieLens dataset, compared with the traditional method.[Limitations] All categories of projects are considered in the model of the user stable interest without special treatments, such as merge and delete.[Conclusions] The experiment result indicates that the recommendation accuracy of the advanced approach is more efficiency, compared with the traditional recommendation algorithm.

收稿日期: 2015-05-04     
:  TP393  
  G35  
基金资助:

本文系国家自然科学基金项目“网页内容真实性评价研究”(项目编号:61171159)和北京市发改委“异构大数据分析挖掘整合技术北京市工程实验室创新能力建设项目”的研究成果之一。

通讯作者: 刘占兵, ORCID: 0000-0003-0085-0761, E-mail: zhanbingliu@126.com。     E-mail: zhanbingliu@126.com
作者简介: 作者贡献声明:肖诗斌: 确定研究方向及研究方法, 提出论文的修订意见; 刘占兵: 算法设计及实验分析, 论文撰写与修订。
引用本文:   
刘占兵, 肖诗斌. 基于用户兴趣模糊聚类的协同过滤算法[J]. 现代图书情报技术, 2015, 31(11): 12-17.
Liu Zhanbing, Xiao Shibin. Collaborative Filtering Recommended Algorithm Based on User's Interest Fuzzy Clustering. New Technology of Library and Information Service, DOI:10.11925/infotech.1003-3513.2015.11.03.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2015.11.03

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