Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/628051
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dc.contributor.authorZhongchen Miaoen_US
dc.contributor.authorJunchi Yanen_US
dc.contributor.authorKai Chenen_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorHongyuan Zhaen_US
dc.contributor.authorWenjun Zhangen_US
dc.date.accessioned2020-05-20T09:39:33Z-
dc.date.available2020-05-20T09:39:33Z-
dc.date.issued2016en_US
dc.identifier.issn2169-3536en_US
dc.identifier.other10.1109/ACCESS.2016.2633282en_US
dc.identifier.urihttp://localhost/handle/Hannan/146838en_US
dc.identifier.urihttp://localhost/handle/Hannan/628051-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractNew item or topic profiling and recommendation are useful yet challenging, especially in face of a “cold-start” situation with sparse user-item ratings for the new arrivals. In this paper, a method of acquiring review opinions of the “sentinel” users on the cold-start items is proposed to elicit those items’ latent profiles, and thus both user-specific ratings and future popularity of the items can be predicted simultaneously. Specifically, such a joint prediction task is formulated as a two-stage optimization problem, and a sentinel user selection algorithm is devised to facilitate effective latent profiles extraction for both item ratings and popularity predictions. Experiments with microblogging and movie data sets corroborate that the proposed method is capable of mitigating the cold-start problem and it outperforms several competitive peer methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7762137.pdfen_US
dc.subjectcold-start|popularity prediction|decision tree|matrix factorization|Recommendationen_US
dc.titleJoint Prediction of Rating and Popularity for Cold-Start Item by Sentinel User Selectionen_US
dc.typeArticleen_US
dc.journal.volume4en_US
dc.journal.titleIEEE Accessen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhongchen Miaoen_US
dc.contributor.authorJunchi Yanen_US
dc.contributor.authorKai Chenen_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorHongyuan Zhaen_US
dc.contributor.authorWenjun Zhangen_US
dc.date.accessioned2020-05-20T09:39:33Z-
dc.date.available2020-05-20T09:39:33Z-
dc.date.issued2016en_US
dc.identifier.issn2169-3536en_US
dc.identifier.other10.1109/ACCESS.2016.2633282en_US
dc.identifier.urihttp://localhost/handle/Hannan/146838en_US
dc.identifier.urihttp://localhost/handle/Hannan/628051-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractNew item or topic profiling and recommendation are useful yet challenging, especially in face of a “cold-start” situation with sparse user-item ratings for the new arrivals. In this paper, a method of acquiring review opinions of the “sentinel” users on the cold-start items is proposed to elicit those items’ latent profiles, and thus both user-specific ratings and future popularity of the items can be predicted simultaneously. Specifically, such a joint prediction task is formulated as a two-stage optimization problem, and a sentinel user selection algorithm is devised to facilitate effective latent profiles extraction for both item ratings and popularity predictions. Experiments with microblogging and movie data sets corroborate that the proposed method is capable of mitigating the cold-start problem and it outperforms several competitive peer methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7762137.pdfen_US
dc.subjectcold-start|popularity prediction|decision tree|matrix factorization|Recommendationen_US
dc.titleJoint Prediction of Rating and Popularity for Cold-Start Item by Sentinel User Selectionen_US
dc.typeArticleen_US
dc.journal.volume4en_US
dc.journal.titleIEEE Accessen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7762137.pdf11.15 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZhongchen Miaoen_US
dc.contributor.authorJunchi Yanen_US
dc.contributor.authorKai Chenen_US
dc.contributor.authorXiaokang Yangen_US
dc.contributor.authorHongyuan Zhaen_US
dc.contributor.authorWenjun Zhangen_US
dc.date.accessioned2020-05-20T09:39:33Z-
dc.date.available2020-05-20T09:39:33Z-
dc.date.issued2016en_US
dc.identifier.issn2169-3536en_US
dc.identifier.other10.1109/ACCESS.2016.2633282en_US
dc.identifier.urihttp://localhost/handle/Hannan/146838en_US
dc.identifier.urihttp://localhost/handle/Hannan/628051-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractNew item or topic profiling and recommendation are useful yet challenging, especially in face of a “cold-start” situation with sparse user-item ratings for the new arrivals. In this paper, a method of acquiring review opinions of the “sentinel” users on the cold-start items is proposed to elicit those items’ latent profiles, and thus both user-specific ratings and future popularity of the items can be predicted simultaneously. Specifically, such a joint prediction task is formulated as a two-stage optimization problem, and a sentinel user selection algorithm is devised to facilitate effective latent profiles extraction for both item ratings and popularity predictions. Experiments with microblogging and movie data sets corroborate that the proposed method is capable of mitigating the cold-start problem and it outperforms several competitive peer methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7762137.pdfen_US
dc.subjectcold-start|popularity prediction|decision tree|matrix factorization|Recommendationen_US
dc.titleJoint Prediction of Rating and Popularity for Cold-Start Item by Sentinel User Selectionen_US
dc.typeArticleen_US
dc.journal.volume4en_US
dc.journal.titleIEEE Accessen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7762137.pdf11.15 MBAdobe PDFThumbnail
Preview File