Uncovering Personalized Implicit Information Based on The Community Detecting Theory of Complex Networks

The massive flow of borrowing information is difficult to discover implicit reader information in Library circulation records. Personalized implicit information detecting theory and technology is topic in information science today. From a new view of complex networks the implicit readers´ information of book circulation in library is discussed. In this paper, firstly the readers are divided into some different communities by two partition algorithms including GN and CP algorithm base on community structure theory; secondly the interest degree of every community member is calculated by fuzzy evaluating rules; finally every new category book can be pushed to the corresponding reader according to every reader´s interest degree. The theory of complex networks is applied in information science, which will both be a new view of personalized implicit information in information science and contribute to the development of complex networks.