Publication:

Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction

Date

2022
Journal article
https://doi.org/10.1109/ACCESS.2022.3219071
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dc.contributor.authorGharahighehi, Alireza
dc.contributor.authorPliakos, Konstantinos
dc.contributor.authorVens, Celine
dc.contributor.imecauthorGharahighehi, Alireza
dc.contributor.imecauthorVens, Celine
dc.contributor.orcidimecGharahighehi, Alireza::0000-0003-1453-1155
dc.contributor.orcidimecVens, Celine::0000-0003-0983-256X
dc.date.accessioned2023-03-02T14:23:02Z
dc.date.available2022-11-30T03:11:09Z
dc.date.available2023-03-02T14:23:02Z
dc.date.embargo2022-11-10
dc.date.issued2022
dc.description.wosFundingTextThis work was supported in part by the Flanders Innovation & Entrepreneurship through the Immosite.com Project, an Innovation Project(Industrial Partners Immosite and g-company) under Project HBC.2020.2674; and in part by the Flemish Government(AI Research Program)
dc.identifier.doi10.1109/ACCESS.2022.3219071
dc.identifier.issn2169-3536
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/40803
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.source.beginpage117189
dc.source.endpage117198
dc.source.issuena
dc.source.journalIEEE ACCESS
dc.source.numberofpages10
dc.source.volume10
dc.title

Addressing the Cold-Start Problem in Collaborative Filtering Through Positive-Unlabeled Learning and Multi-Target Prediction

dc.typeJournal article
dspace.entity.typePublication
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