iHITS: Extending HITS for Personal Interests Profiling

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iHITS: Extending HITS for Personal Interests Profiling
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 evalu...
Ziming Zhuang
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where AINA
Authors Ziming Zhuang
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