Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00530-012-0267-z
DC FieldValue
dc.titleVideo recommendation over multiple information sources
dc.contributor.authorZhao, X.
dc.contributor.authorYuan, J.
dc.contributor.authorWang, M.
dc.contributor.authorLi, G.
dc.contributor.authorHong, R.
dc.contributor.authorLi, Z.
dc.contributor.authorChua, T.-S.
dc.date.accessioned2013-07-04T08:11:51Z
dc.date.available2013-07-04T08:11:51Z
dc.date.issued2013
dc.identifier.citationZhao, X., Yuan, J., Wang, M., Li, G., Hong, R., Li, Z., Chua, T.-S. (2013). Video recommendation over multiple information sources. Multimedia Systems 19 (1) : 3-15. ScholarBank@NUS Repository. https://doi.org/10.1007/s00530-012-0267-z
dc.identifier.issn09424962
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40766
dc.description.abstractVideo recommendation is an important tool to help people access interesting videos. In this paper, we propose a universal scheme to integrate rich information for personalized video recommendation. Our approach regards video recommendation as a ranking task. First, it generates multiple ranking lists by exploring different information sources. In particular, one novel source user's relationship strength is inferred through the online social network and applied to recommend videos. Second, based on multiple ranking lists, a multi-task rank aggregation approach is proposed to integrate these ranking lists to generate a final result for video recommendation. It is shown that our scheme is flexible that can easily incorporate other methods by adding their generated ranking lists into our multi-task rank aggregation approach. We conduct experiments on a large dataset with 76 users and more than 11,000 videos. The experimental results demonstrate the feasibility and effectiveness of our approach. © Springer-Verlag 2012.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s00530-012-0267-z
dc.sourceScopus
dc.subjectMulti-task rank aggregation
dc.subjectOnline social network
dc.subjectRich information
dc.subjectVideo recommendation
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/s00530-012-0267-z
dc.description.sourcetitleMultimedia Systems
dc.description.volume19
dc.description.issue1
dc.description.page3-15
dc.description.codenMUSYE
dc.identifier.isiut000314297800002
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

13
checked on Dec 6, 2022

WEB OF SCIENCETM
Citations

12
checked on Nov 28, 2022

Page view(s)

364
checked on Nov 24, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.