Presenter ChunPing Wu Authors Jeffrey Davcitz Jiye Yu Sugato Basu David Gutelius Alexandra Harris KDD 2007 國立雲林科技大學 National Yunlin University of Science and Technology ID: 804947
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ILINK:Search and Routing in Social Networks
Presenter : Chun-Ping Wu Authors :Jeffrey Davcitz, Jiye Yu, Sugato Basu, David Gutelius, Alexandra Harris
KDD 2007
國立雲林科技大學
National Yunlin University of Science and Technology
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Slide2OutlineMotivationObjective
ILINKFAQTORYLearning FrameworkCase StudyConclusionComments2
Slide3MotivationThis paper focuses on the problem of modeling how social networks accomplish tasks through peer production
style collaboration.3
Slide4Objective To propose a general interaction model for the underlying social networks and then a specific model(I
LINK) for social search and message routing.4
Slide5ILINKThe ILINK model has various components.Node
Each node represents a user in the network, and has an associated profile.SupernodeA database D storing all the past message streams.Profile parameters E, R, F for all the nodes in the network.The set of all possible topics T.MessageA message m is routed between a node and the supernode.5
Slide6FAQTORYFAQtory system is implemented as a three-tiered client-server application.
The client/front tier facilitates interactions between the nodes and the FAQtory server via APIs.Web browserEmail clientsInstant messenger6
Slide7FAQTORY-Routing Mechanism
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Slide8Learning FrameworkThe learning framework in this case has to solve a set of interrelated learning problems.
Heterogeneous topicsCold-start problemPrivacy issuesPrior knowledgeMessage matchingScalabilityIncremental learning8
Slide9Case Study and ApplicationsCalo
Test PilotThe FAQtory system of ILINK was developed as part of CALO, an adaptive cognitive system funded under DARPA’s PAL(Perceptive Assistant that Learns) program.PatoonLeaderProvided with two features that leverage ILINK technology: “Suggested Discussions” and the “Moderator’s Assistant”.Other Message ApplicationsSmart Rss Filter
Message Routing for Advertising9
Slide10Conclusion
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The web has made large peer production frameworks possible, which are unprecedented in terms of both scale and intensity.
The importance of making such models work for real, ongoing web-based collaboration efforts is a growing issue and a wonderful challenge for machine learning and data mining techniques.
Slide11Comments
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Advantage
The idea is good.
Drawback
Many practical
and theoretical issues remain.
Application
Peer production