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- Publisher
- Association for Computing Machinery
- Copyright
- Copyright © 2010 by ACM Inc.
- ISSN
- 1556-4681
- DOI
- 10.1145/1870096.1870098
- Publisher site
- See Article on Publisher Site
Abstract
A Combination Approach to Web User Pro ling JIE TANG Tsinghua University LIMIN YAO University of Massachusetts Amherst DUO ZHANG University of Illinois at Urbana-Champaign and JING ZHANG Tsinghua University In this article, we study the problem of Web user pro ling, which is aimed at nding, extracting, and fusing the œsemantic -based user pro le from the Web. Previously, Web user pro ling was often undertaken by creating a list of keywords for the user, which is (sometimes even highly) insuf cient for main applications. This article formalizes the pro ling problem as several subtasks: pro le extraction, pro le integration, and user interest discovery. We propose a combination approach to deal with the pro ling tasks. Speci cally, we employ a classi cation model to identify relevant documents for a user from the Web and propose a Tree-Structured Conditional Random Fields (TCRF) to extract the pro le information from the identi ed documents; we propose a uni ed probabilistic model to deal with the name ambiguity problem (several users with the same name) when integrating the pro le information extracted from different sources; nally, we use a probabilistic topic model to model the extracted user pro
Journal
ACM Transactions on Knowledge Discovery from Data (TKDD) – Association for Computing Machinery
Published: Dec 1, 2010