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dc.contributor.advisorYu, Jian
dc.contributor.advisorYongchareon, Sira
dc.contributor.authorWu, Zhentao
dc.date.accessioned2019-11-13T22:43:49Z
dc.date.available2019-11-13T22:43:49Z
dc.date.copyright2019
dc.identifier.urihttp://hdl.handle.net/10292/13001
dc.description.abstractThe sparseness of Mashup-API rating matrix coupled with cold-start and scalability issues have been identified as the most critical challenges that affect most Collaborative filtering based Web APIs recommendation solution. Sparseness deteriorates the rating prediction accuracy. Several Web-API recommendation approaches employ basic collaborative filtering technique which operates on second-order matrices or tensors by decomposing the Mashup-API interaction matrix into two low-rank matrix approximations, and then make prediction based on the factorized tensors. While most existing CF, Matrix factorization-based Web-API recommendation approaches have shown promising improvement in recommendation results, one limitation is that they only focuson2-dimensionaldatamodelinwhichhistoricalinteractionbetweenMashup-API are mainly used. However, recent works in recommendation domain show that by incorporating additional information into the rating data, Web-API rating prediction accuracy can be enhanced. Inspired by these works, this research proposes a collaborative Filtering method based Tensors factorization, an extension of Matrix factorization that exploits the ternary relation among three key entities in Web service ecosystem Mashup-API-Proximity. Modelling the Web-API rating data with Tensor decomposition technique enables incorporation proximity information as third additional entity into Web service recommendation application to improve prediction accuracy. Specifically, we employ High Order Singular Value Decomposition approach with regularization term to extend the traditional Mashup-API matrix into Mashup-API-Proximity tensors. 5 Experimental analysis on Programmable Web dataset shows promising results compare with some state-of-the-art approaches.en_NZ
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.subjectTensors Factorizationen_NZ
dc.subjectProgrammable Weben_NZ
dc.subjectHigh Order Singular Value Decompositionen_NZ
dc.subjectRecommendationen_NZ
dc.subjectMashupsen_NZ
dc.subjectWeb-API recommendationen_NZ
dc.titleIncorporating Service Proximity Into Web Service Recommendation Via Tensors Decompositionen_NZ
dc.typeThesisen_NZ
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciencesen_NZ
dc.rights.accessrightsOpenAccess
dc.date.updated2019-11-13T11:50:36Z


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