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Most of the traditional community detection algorithms cannot balance partitioning effect and complexity well. So， this paper presents a new evaluation standard of single community called group density. Based on the group density， a community detection algorithm based on agglomeration is proposed. The algorithm continues to integrate small communities， and makes the community structure of the network develop in the direction of maximizing average group density. Modularity is employed to detect the partitioning effect of the algorithm. Experimental results demonstrate that the new algorithm outperforms the traditional GN， Fast Newman， LPA algorithms in multiple data sets， which shows that the algorithm proposed has better partitioning effect and lower time complexity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]