基于二分网络社团划分的推荐算法  

Recommendation Algorithm Based on Community Detection in Bipartite Networks

作　　者：陈东明[1] 严燕斌 黄新宇 王冬琦[1] CHEN Dong-ming;YAN Yan-bin;HUANG Xin-yu;WANG Dong-qi(School of Software,Northeastern University,Shenyang 110169,China)

机构地区：[1]东北大学软件学院,辽宁沈阳110169

出　　处：《东北大学学报（自然科学版）》2018年第8期1103-1107,共5页Journal of Northeastern University(Natural Science)

基　　金：辽宁省自然科学基金资助项目(20170540320);辽宁省教育厅科学研究项目(L20150167)

摘　　要：传统的基于用户的协同过滤(User-based CF)推荐算法的推荐效率随着数据的不断增加而降低.本文在User-based CF算法中引入二分网络社团发现理论,提出一种基于二分网络社团划分的推荐算法(RACD).首先通过用户与项目之间的关系建立用户-项目二分网络,然后通过RACD对该网络进行社团划分,得到用户的社团信息,最后通过同一社团中的其他用户对目标用户进行项目的推荐.在经典网络数据集上的实验结果表明,RACD能够有效提高推荐系统实时推荐效率.The efficiency of traditional user-based collaborative filtering（ user-based CF）recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection（ RACD） in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly,the user-item rating matrix is mapped into user-item bipartite network. Then, the community information of each user is obtained by using RACD to divide the user-item network. Finally,the items are recommended to the target user according to other users in the same community.Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system.

关 键 词：推荐算法 二分网络 社团划分 协同过滤 复杂网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]