Fu Yuchen, Liu Quan, Cui Zhiming
Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, Jiangsu 215123, China ; School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.
School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.
ScientificWorldJournal. 2014;2014:295931. doi: 10.1155/2014/295931. Epub 2014 Apr 13.
The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database.
基于二分网络的推荐算法在准确性和多样性方面优于传统方法，这证明考虑推荐系统的网络拓扑结构有助于我们提高推荐结果。然而，现有算法主要关注整体拓扑结构，而这些局部特征在协同推荐过程中也可能发挥重要作用。因此，根据协同推荐系统的数据特征和应用需求，我们提出了一种基于标签传播的链接社区划分算法和一种基于二分社区的协同推荐算法。然后我们设计了数值实验，在基准数据库和真实数据库下验证算法的有效性。
