Divergence Based Non-Negative Matrix Factorization for top-N Recommendations

dc.contributor.author Haque, Md. Enamul
dc.contributor.author Zobaed, SM
dc.contributor.author Tozal, Mehmet Engin
dc.contributor.author Raghavan, Vijay
dc.date.accessioned 2019-01-02T23:41:36Z
dc.date.available 2019-01-02T23:41:36Z
dc.date.issued 2019-01-08
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.055
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59485
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Science and Digital Collaborations
dc.subject Collaboration Systems and Technologies
dc.subject K-L divergence
dc.subject Matrix factorization
dc.subject Top N recommendation
dc.title Divergence Based Non-Negative Matrix Factorization for top-N Recommendations
dc.type Conference Paper
dc.type.dcmi Text
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