Study of item matching algorithm based on bipartite graph joint clustering for technology transaction platform
Ming Qiao Zhu, Nana Huang, Cairong Yan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2013
For How to match supply items with demand items is the most important on technology transaction platform. A matching algorithm based on bipartite graph is proposed in this paper. Firstly, by abstracting characters the suppliers and demanders can be clustered into groups based on bipartite graph joint clustering method. Then, an incidence matrix between items in each group is built which is used to find the optimal matching relation. The simulate experiment results showed that the algorithm can return the item pairs of biggest transaction probability so as to make the Technology transaction platform efficient and profit.