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dc.contributor.authorSymeonidis P
dc.contributor.editor
dc.date.accessioned2019-03-08T08:23:57Z
dc.date.available2019-03-08T08:23:57Z
dc.date.issued2009
dc.identifier.isbn978-1-441-90220-7
dc.identifier.issn1571-5736
dc.identifier.urihttp://dx.doi.org/10.1007/978-1-4419-0221-4_39
dc.identifier.urihttp://link.springer.com/chapter/10.1007/978-1-4419-0221-4_39
dc.identifier.urihttp://hdl.handle.net/10863/9054
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 recommend 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 model to capture the three types of entities that exist in a social tagging system: users, items, and tags. These data are represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) method. We perform experimental comparison of the proposed method against a baseline user recommendation algorithm with a real data set (BibSonomy), attaining significant improvements. © 2009 International Federation for Information Processing.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation5th IFIP Conference on Artificial Intelligence Applications and Innovations ; Thessaloniki : 23.4.2009 - 25.3.2009
dc.relation.ispartofseriesInternational Federation for Information Processing -Publications- Ifip;
dc.rights
dc.titleUser recommendations based on tensor dimensionality reductionen_US
dc.typeBook chapteren_US
dc.date.updated2019-03-08T03:00:52Z
dc.publication.titleArtificial Intelligence Applications and Innovations: Proceedings of the 5th IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI'2009), April 23-25, 2009, Thessaloniki, Greece
dc.language.isiEN-GB
dc.description.fulltextopenen_US


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