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AIC Seminar Series

Machine Learning in Web 2.0: Analyzing Dynamic Content-driven Social Networks

Sugato BasuAritificial Intelligence Center, SRI International[Home Page]

Date:  2006-11-30 at 11:00

Location:  EK255  (Directions)

   Abstract

In the last decade, machine learning and data mining techniques have seen widespread successful application to different Internet technologies, including web search, product recommendation, spam detection, spelling correction, and news clustering. However, the web is fast undergoing a paradigm shift, moving from being a mechanism for delivering static web-content in the existing Web 1.0 model to a platform facilitating dynamic collaborative content creation in the emerging Web 2.0 paradigm. This trend is reflected in the growing popularity of new social web-services, for example, tagging (Flickr) compared to photo editing (Ofoto), and blogging (Blogger) compared to homepage hosting (Geocities). This talk will outline how this new emphasis on rapid creation and sharing of consumer-generated data (CGM) over large social networks has given rise to dynamic content-driven social networks, and a new set of challenging machine learning problems in this context. Focusing on a project (iLink) that the speaker is currently working on, the talk will discuss research problems like online learning of topic models over streaming text, large-scale topic analysis over social networks, and learning to route messages in a social query model.

   Bio for Sugato Basu

My main areas of research interest are machine learning, data mining, information retrieval, statistical pattern recognition and optimization, with applications to analysis of text and biological data. I am currently working as a research scientist on the CALO project.

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