Session segmentation method based on COBWEB

Session segmentation can not only facilitate further study of users´ interest mining but also act as the foundation of other retrieval researches based on users´ complicated search behaviors. This paper proposes session boundary discrimination model (the binary classification tree) utilizing time interval and query likelihood on the basis of COBWEB. The model has prominently improved recall ratio, precision ratio and value F to more than 90 percent and particularly the value F for yes class rises compared with previous study. It is an incremental algorithm that can deal with large scale data, which will be perfectly applied into user interest mining. Owing to its good performance in session boundary discrimination, the application of the model can serve as a tool in fields like personalized information retrieval, query suggestion, search activity analysis and other fields which have connection with search results improvement.