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Collaborative ranking for local preferences

Author(s): Kapicioglu, B; Rosenberg, DS; Schapire, Robert E; Jebara, T

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dc.contributor.authorKapicioglu, B-
dc.contributor.authorRosenberg, DS-
dc.contributor.authorSchapire, Robert E-
dc.contributor.authorJebara, T-
dc.date.accessioned2021-10-08T19:47:20Z-
dc.date.available2021-10-08T19:47:20Z-
dc.date.issued2014-01-01en_US
dc.identifier.citationKapicioglu, B, Rosenberg, DS, Schapire, RE, Jebara, T. (2014). Collaborative ranking for local preferences. Journal of Machine Learning Research, 33 (466 - 474en_US
dc.identifier.issn1532-4435-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1p241-
dc.description.abstractFor many collaborative ranking tasks, we have access to relative preferences among subsets of items, but not to global preferences among all items. To address this, we introduce a matrix factorization framework called Collaborative Local Ranking (CLR). We justify CLR by proving a bound on its generalization error, the first such bound for collaborative ranking that we know of. We then derive a simple alternating minimization algorithm and prove that its running time is independent of the number of training examples. We apply CLR to a novel venue recommendation task and demonstrate that it outperforms state-of-the-art collaborative ranking methods on real-world data sets.en_US
dc.format.extent466 - 474en_US
dc.language.isoen_USen_US
dc.relation.ispartofJournal of Machine Learning Researchen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleCollaborative ranking for local preferencesen_US
dc.typeConference Articleen_US
dc.identifier.eissn1533-7928-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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