Please use this identifier to cite or link to this item: https://repository.cihe.edu.hk/jspui/handle/cihe/539
DC FieldValueLanguage
dc.contributor.authorXie, Haoran-
dc.contributor.authorWang, Philips Fu Lee-
dc.contributor.authorWong, Tak Lam-
dc.contributor.otherLam, W.-
dc.date.accessioned2021-04-14T02:48:16Z-
dc.date.available2021-04-14T02:48:16Z-
dc.date.issued2016-
dc.identifier.urihttps://repository.cihe.edu.hk/jspui/handle/cihe/539-
dc.description.abstractWe have developed a framework for jointly conducting collaborative filtering and distance metric learning based on regularized singular value decomposition (RSVD), which discovers the user matrix and item matrix in the low rank space. Our approach is able to solve RSVD and simultaneously learn the parameters of Mahalanobis distance considering the ratings given by similar users and dissimilar users. One characteristic of our approach is that the learned model can be effectively applied to rating prediction and other relevant applications such as trust prediction, resulting in a solution which is coherent and optimal to both tasks. Another characteristic is that social community information and similarity information can be easily considered in our framework. We have conducted extensive experiments on rating prediction using real-world datasets to evaluate our framework. We have also compared our framework with other existing works to illustrate the effectiveness. Experimental results show that our framework achieves a promising prediction performance and outperforms the existing works.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.titleA joint framework for collaborative filtering and metric learningen_US
dc.typeconference proceedingsen_US
dc.relation.publicationInformation Retrieval Technology (12th Asia Information Retrieval Societies Conference, AIRS 2016) Proceedingsen_US
dc.identifier.doi10.1007/978-3-319-48051-0_14-
dc.contributor.affiliationSchool of Computing and Information Sciences-
dc.relation.isbn9783319480503en_US
dc.description.startpage184en_US
dc.description.endpage196en_US
dc.cihe.affiliatedYes-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.grantfulltextnone-
item.openairetypeconference proceedings-
crisitem.author.deptYam Pak Charitable Foundation School of Computing and Information Sciences-
crisitem.author.deptRita Tong Liu School of Business and Hospitality Management-
crisitem.author.deptYam Pak Charitable Foundation School of Computing and Information Sciences-
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