A Unified Video Recommendation by Cross-Network User Modeling
文献类型：期刊论文
| 作者 | Yan, Ming1,2 ; Sang, Jitao1,3; Xu, Changsheng1,2; Hossain, M. Shamim4
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| 刊名 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
 |
| 出版日期 | 2016-08-01 |
| 卷号 | 12期号:4页码:1-24 |
| 关键词 | Algorithms Experimentation Performance Personalized Video Recommendation Cross-network Collaboration User Modeling |
| DOI | 10.1145/2957755 |
| 文献子类 | Article |
| 英文摘要 | Online video sharing sites are increasingly encouraging their users to connect to the social network venues such as Facebook and Twitter, with goals to boost user interaction and better disseminate the high-quality video content. This in turn provides huge possibilities to conduct cross-network collaboration for personalized video recommendation. However, very few efforts have been devoted to leveraging users' social media profiles in the auxiliary network to capture and personalize their video preferences, so as to recommend videos of interest. In this article, we propose a unified YouTube video recommendation solution by transferring and integrating users' rich social and content information in Twitter network. While general recommender systems often suffer from typical problems like cold-start and data sparsity, our proposed recommendation solution is able to effectively learn from users' abundant auxiliary information on Twitter for enhanced user modeling and well address the typical problems in a unified framework. In this framework, two stages are mainly involved: (1) auxiliary-network data transfer, where user preferences are transferred from an auxiliary network by learning cross-network knowledge associations; and (2) cross-network data integration, where transferred user preferences are integrated with the observed behaviors on a target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in terms of accuracy, but also in improving the diversity and novelty of the recommended videos. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000382877500007 |
| 资助机构 | National Basic Research Program of China(2012CB316304) ; National Natural Science Foundation of China(61432019 ; Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia(RGP-228) ; 61225009 ; 61303176 ; 61272256 ; 61373122 ; 61332016) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12630]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China 4.King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh 11543, Saudi Arabia |
| 推荐引用方式 GB/T 7714 | Yan, Ming,Sang, Jitao,Xu, Changsheng,et al. A Unified Video Recommendation by Cross-Network User Modeling[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2016,12(4):1-24. |
| APA | Yan, Ming,Sang, Jitao,Xu, Changsheng,&Hossain, M. Shamim.(2016).A Unified Video Recommendation by Cross-Network User Modeling.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,12(4),1-24. |
| MLA | Yan, Ming,et al."A Unified Video Recommendation by Cross-Network User Modeling".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 12.4(2016):1-24. |
入库方式： OAI收割
来源：自动化研究所
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