Prestige based information ranking model in heterogeneous information networks
Chen Yunzh · Computer Engineering and Applications Journal · 2014
The amount of data is increasing rapidly with the continuous development of Internet. There also exists a large number of redundant information and many complex relationships between data on the Internet. Ranking methods are now facing serious problem that redundant information makes the results bad. This paper focuses on how to get authoritative, diverse and understandable ranking results on heterogeneous information network. The paper proposes a ranking model by simultaneously exploring prestige and diversity. The model constructs a heterogeneous information network based on the data, and then uses MutualRank to learn the prestige of the objects using PDRank model which combines the learned prestige and diversity. The ranking model uses the homogeneous and heterogeneous relationship between objects, and eliminates redundant information in ranking result. Experiments show that MutualRank is better than PageRank on modeling prestige, and the ranking results based on two-phase ranking model are superior to the existing base method.