Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00530-012-0267-z
Title: Video recommendation over multiple information sources
Authors: Zhao, X.
Yuan, J.
Wang, M.
Li, G. 
Hong, R.
Li, Z.
Chua, T.-S. 
Keywords: Multi-task rank aggregation
Online social network
Rich information
Video recommendation
Issue Date: 2013
Source: Zhao, 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
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. © Springer-Verlag 2012.
Source Title: Multimedia Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/40766
ISSN: 09424962
DOI: 10.1007/s00530-012-0267-z
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