Résumé: In this article, we study the problem of Web user profiling, which is aimed at finding, extracting, and fusing the "semantic"-based user profile from the Web. Previously, Web user profiling was often undertaken by creating a list of keywords for the user, which is (sometimes even highly) insufficient for main applications. This article formalizes the profiling problem as several subtasks: profile extraction, profile integration, and user interest discovery. We propose a combination approach to deal with the profiling tasks. Specifically, we employ a classification model to identify relevant documents for a user from the Web and propose a Tree-Structured Conditional Random Fields (TCRF) to extract the profile information from the identified documents; we propose a unified probabilistic model to deal with the name ambiguity problem (several users with the same name) when integrating the profile information extracted from different sources; finally, we use a probabilistic topic model to model the extracted user profiles, and construct the user interest model. Experimental results on an online system show that the combination approach to different profiling tasks clearly outperforms several baseline methods. The extracted profiles have been applied to expert finding, an important application on the Web. Experiments show that This work was done when L. Yao and D. Zhang were studying in Tsinghua University. This work is supported by the Natural Science Foundation of China (No. 60703059, No. 60973102), Chinese National Key Foundation Research (No. 60933013), National High-Tech R&D Program (No. 2009AA01Z138), and Chinese Young Faculty Research Fund (No. 20070003093). Authors' addresses: J. Tang (corresponding author) and J. Zhang, Room 1-308, FIT Building, Tsinghua University, Beijing, 100084, China; email: [email protected], zhangjing@keg. cs.tsinghua.edu.cn; D. Zhang, 1125 Siebel Center for Computer Science, University of Illinois at Urbana-Champaign, 201 N. Goodwin Ave., Urbana, IL 61801; email: [email protected]; L. Yao, Department of Computer Science, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA 01003; email: [email protected]. Permission to make digital or hard copies part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from the Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax +1 (212) 869-0481, or [email protected]. _c 2010 ACM 1556-4681/2010/12-ART2

Langue: Anglais
Thème Informatique

Mots clés:
Information extraction
Social networks
Topic modeling
User profiling
Name disambiguation
Text mining