An Improved Mining Strategy of Preferred Paths in Web Applications Based on RBF Neural Network
Xiang Li, Ningjiang Chen, Qiqi Xie, Shilong Dong, Lirong Zhu, Ying Ching Tan · 2013
Traditional preferred path mining algorithms cannot accurately identify the level of user interest in a web page, and they are rather complex. In order to solve these problems, the paper proposed an improved calculation method which evaluated the level of user interest in a web page and a mining algorithm of preferred path in the cloud computing environment. Firstly, the paper used Preference Degree to evaluate the user interest in a web page, and then designed a model based on RBF neural network to predict preference degree. This model adequately considered the some key factors such as visit quantity, access time, page bytes, and the nonlinearity relationships among these factors. Secondly, based on preference degree predicting model, the paper presented a new mining algorithm. The algorithm used locations of non-preferred URLs to get the preferred path. The experimental results show that the preference degree prediction model can identify user interest more accurately and comprehensively. In addition, the newly proposed mining algorithm of preferred paths has a lower time complexity and saves more time.