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dc.contributor.authorZhang, Xiaoyuen_US
dc.date.accessioned2014-03-14T20:45:24Z
dc.date.available2014-03-14T20:45:24Z
dc.date.issued2009-09-11en_US
dc.identifier.otheretd-09142009-135014en_US
dc.identifier.urihttp://hdl.handle.net/10919/35059
dc.description.abstractOnline Knowledge Communities, also known as online forum, are popular web-based tools that allow members to seek and share knowledge. Documents to answer varieties of questions are associated with the process of knowledge exchange. The social network of members in an Online Knowledge Community is an important factor to improve search precision. However, prior ranking functions donâ t handle this kind of document with using this information. In this study, we try to resolve the problem of finding authoritative documents for a user query within an Online Knowledge Community. Unlike prior ranking functions which consider either content based feature, hyperlink based feature, or document structure based feature, we explored the Online Knowledge Community social network structure and members social interaction activities to design features that can gauge the two major factors affecting user knowledge adoption decision: argument quality and source credibility. We then design a customized Genetic Algorithm to adjust the weights for new features we proposed. We compared the performance of our ranking strategy with several others baselines on a real world data www.vbcity.com/forums/. The evaluation results demonstrated that our method could improve the user search satisfaction with an obviously percentage. At the end, we concluded that our approach based on knowledge adoption model and Genetic Algorithm is a better ranking strategy in the Online Knowledge Community.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartthesis_1028.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectOnline Knowledge Communityen_US
dc.subjectSocial Networken_US
dc.subjectGenetic Algorithmen_US
dc.subjectInformation Systemen_US
dc.subjectInformation Retrievalen_US
dc.subjectKnowledge Adoption Modelen_US
dc.titleEffective Search in Online Knowledge Communities: A Genetic Algorithm Approachen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairFan, Weiguo Patricken_US
dc.contributor.committeememberWang, G. Alanen_US
dc.contributor.committeememberMarathe, Madhav V.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09142009-135014/en_US
dc.date.sdate2009-09-14en_US
dc.date.rdate2009-11-02
dc.date.adate2009-11-02en_US


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