Personalized Recommendation of E-Commerce Website Category Hierarchy Based on Web Usage Mining and Multidimensional Scaling

The purpose of this paper is to study personalized needs of e-commerce website category hierarchy based on users' mental models by means of Multidimensional Scaling and Web Usage Mining. The users' browsing category paths in an e-commerce website is extracted based on the Web Usage Mining, and the Multidimensional Scaling was used to probe the structure and composition of the users' mental models of website category hierarchy based on their browsing category paths, at last, users' personalized needs can be identified. Three million web log data records were collected for experimental study. The experimental results show the proposed method is efficient to discover users' personalized needs of expected category hierarchy based on large scale web log data automatically and efficiently.