Ever since the boom of World Wide Web, profiling online users' interests has become an important task for content providers. The traditional approach involves manual entry of users' data, which requires intensive labor and time. Recent approaches utilize machine learning and clustering techniques to build the profiles, by analyzing the content of the Web pages visited by the users. Because such solutions rely heavily on the textual information, although they are capable of differentiating different topics of interests, it remains a difficult task to determine the users' different levels of interests in a given topic as well as gauge the shift of interests over time.

In this paper, we propose iHITS, which is an extension to the HITS (Hypertext-Induced Topic Search) algorithm. The algorithm automatically determines a ranked list of user?s interests through link analysis on Web pages that the user visited. The visit pattern is obtained from the browsing history. We evaluate our approach by comparing automatically-generated interests profiles of the users with users? manual entry to examine its accuracy and effectiveness. Our evaluation shows that the approach is promising and achieves satisfactory results. Our study introduces a novel approach to build a user-interests profiling systems with the capability to automatically capture and rank users? browsing interests preference.