Research on intelligent recommended algorithms of personalized digital library

In this paper, the intelligent recommended algorithms of association rule and collaborative filtering (CF) technology are designed to solve the problem of low utilization and waste time in the usage of digital library. Neighbor user sets are generated by CF and user-user association firstly, and then recommended lists are generated by URL association rule based on the history of neighbor user set. The neighbor user sets generated by combination of association rule and CF can fit requirements of recommended system, and the system can avoid generate meaningless rule according to mining the history of neighbor user sets. Experiments show that the utilization rate of digital book can be raised and time of search digital book can be reduced by using this recommended system.