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Multimedia Systems
Erschienen in: Multimedia Systems 1/2013

01.02.2013 | Regular Paper

Video recommendation over multiple information sources

verfasst von: Xiaojian Zhao, Jin Yuan, Meng Wang, Guangda Li, Richang Hong, Zhoujun Li, Tat-Seng Chua

Erschienen in: Multimedia Systems | Ausgabe 1/2013

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Abstract

Video 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.
Vorheriger Artikel Guest editorial: selected papers from ICIMCS 2011
Nächster Artikel Multi-label multi-instance learning with missing object tags

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Metadaten
Titel
Video recommendation over multiple information sources
verfasst von
Xiaojian Zhao
Jin Yuan
Meng Wang
Guangda Li
Richang Hong
Zhoujun Li
Tat-Seng Chua
Publikationsdatum
01.02.2013
Verlag
Springer-Verlag
Erschienen in
Multimedia Systems / Ausgabe 1/2013
Print ISSN: 0942-4962
Elektronische ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-012-0267-z

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