基于社区划分的学术论文推荐模型

Academic paper recommendation model based on community partition

针对学术社交网络独有的社交性,构建了基于社区划分的学术论文推荐模型。模型选择社区复杂好友关系网络图中最大连通分量作为数据处理逻辑单元,在此基础上进行核心关系网划分,并采用非参数控制的方式,在所建立的核心关系网内建立标签,在学术社交网络中通过标签传播进行社区划分,根据社区划分结果在社区内部的用户之间推荐学术论文。该社区划分算法与经典社区划分算法在人工网络上进行仿真实验,结果表明该算法在不同特征的人工网络上皆能取得良好的社区发现质量。

An academic paper recommendation model based on community partition was proposed according to sociability in social network. The model regarded the largest connected component in complex network as the logic unit in data processing,and divided up the largest connected component into non-intersect kernel sub-network. The labels would be established according to kernel sub-network by non-parameter control mode. Communities were divided in scholar social network through label propagation,and academic papers were recommended among the users in the communities by the results of the community partition. The proposed community partition method was compared with the classic community partition method in the experiments on artificial network. The experimental results show that the proposed method can achieve good community partition qualities on different characteristic artificial networks.