Stability Analysis for Users' Web Preference

摘要

Advanced personalized e-applications require comprehensive knowledge about their user's preference in order to provide individual product recommendations and custom-tailored product offers. Much research has been conducted using Web logs to infer user preferences and predict users' behavior. However, little research measures the stability of user preferences over time. In this paper, the index named Web stability coefficient (ST) based on a comprehensive analysis of Web access records is proposed to represent the stability of user's preference. Then we used k-means clustering algorithm to assign users into different groups, which is computed based on the values of vector representation of user's ST. By analyzing user patterns, we present some interesting conclusions that facilitate us to better understand behavioral characteristics of Web user.