基于二分网络社团划分的推荐算法

Recommendation Algorithm Based on Community Detection in Bipartite Networks

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.