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.