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dc.contributor.authorSymeonidis P
dc.contributor.authorNanopoulos A
dc.contributor.authorManolopoulos Y
dc.contributor.editor
dc.date.accessioned2019-03-08T08:25:40Z
dc.date.available2019-03-08T08:25:40Z
dc.date.issued2010
dc.identifier.issn1041-4347
dc.identifier.urihttp://dx.doi.org/10.1109/TKDE.2009.85
dc.identifier.urihttp://ieeexplore.ieee.org/abstract/document/4815246/
dc.identifier.urihttp://hdl.handle.net/10863/9056
dc.description.abstractSocial Tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the Kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision. © 2006 IEEE.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relation
dc.rights
dc.subjectHOSVDen_US
dc.subjectSocial tagsen_US
dc.subjectTensorsen_US
dc.subjectRecommender systemsen_US
dc.titleA unified framework for providing recommendations in social tagging systems based on ternary semantic analysisen_US
dc.typeArticleen_US
dc.date.updated2019-03-08T03:00:43Z
dc.publication.title
dc.language.isiEN-GB
dc.journal.titleIEEE Transactions on Knowledge and Data Engineering
dc.description.fulltextopenen_US


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