Mining Social Tags to Predict Mashup Patterns

dc.contributor.authorEl-Goarany, Khaleden
dc.contributor.committeechairKulczycki, Gregory W.en
dc.contributor.committeememberBlake, M. Brianen
dc.contributor.committeememberFrakes, William B.en
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-03-14T21:46:28Zen
dc.date.adate2010-11-11en
dc.date.available2014-03-14T21:46:28Zen
dc.date.issued2010-09-10en
dc.date.rdate2010-11-11en
dc.date.sdate2010-09-24en
dc.description.abstractIn this thesis, a tag-based approach is proposed for predicting mashup patterns, thus deriving inspiration for potential new mashups from the community's consensus. The proposed approach applies association rule mining techniques to discover relationships between APIs and mashups based on their annotated tags. The importance of the mined relationships is advocated as a valuable source for recommending mashup candidates while mitigating common problems in recommender systems. The proposed methodology is evaluated through experimentation using a real-life dataset. Results show that the proposed mining approach achieves prediction accuracy with 60% precision and 79% recall improvement over a direct string matching approach that lacks the mining information.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-09242010-002320en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09242010-002320/en
dc.identifier.urihttp://hdl.handle.net/10919/44897en
dc.publisherVirginia Techen
dc.relation.haspartEl-Goarany_K_T_2010.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSocial Tagsen
dc.subjectMashupsen
dc.subjectWeb Miningen
dc.subjectRecommender Systemsen
dc.titleMining Social Tags to Predict Mashup Patternsen
dc.typeThesisen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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