In this paper, Assistant Professor Catherine Yang proposes a simple, yet powerful approach to profile users’ web browsing behavior for the purpose of user identification. The importance of being able to identify users can be significant given a wide variety of applications in electronic commerce, such as product recommendation, personalized advertising, etc.

Yang creates user profiles capturing the strength of users’ behavioral patterns, which can be used to identify users. Her experiments indicate that these profiles can be more accurate at identifying users than decision trees when sufficient web activities are observed, and can achieve higher efficiency than Support Vector Machines. The comparisons demonstrate that profile-based methods for user identification provide a viable and simple alternative to this problem.