Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation

doi:10.1109/TMM.2018.2839530

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多媒体计算与图形学团队

	Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation
	Sang, Jitao 1,2; Yan, Ming 3; Xu, Changsheng4
刊名	IEEE TRANSACTIONS ON MULTIMEDIA
	2018-12-01
卷号	20 期号:12 页码:3439-3451
关键词	Cross-OSN association dynamic user modeling cold-start recommendation
ISSN号	1520-9210
DOI	10.1109/TMM.2018.2839530
通讯作者	Sang, Jitao(jtsang@bjtu.edu.cn)
英文摘要	Online social networks (OSNs) have become an essential part of people's daily life, and an increasing number of users are now using multiple OSNs for different social media services simultaneously. As a result, user's interests and preferences usually distribute in different OSNs. While most of the existing work mainly aggregates the distributed user behaviors or features directly, recently very few efforts have been focused on understanding the crass-OSN association from collective user behaviors. In this paper, we go one step further to consider the dynamic characteristic of user behaviors and propose a dynamic cross-OSN association mining framework. In this framework, dynamic user modeling is first conducted to capture the drift of user interest in each OSN. A session-based factorization method is then proposed to establish the cross-OSN association in a dynamic manner, by incrementally updating the derived association each time a new session of data arrives. Based on the derived dynamic association, we finally design a cold-start YouTube video recommendation application, by only utilizing users' behaviors in Twitter. Experiments are conducted using real-world user data from Twitter and YouTube. The results demonstrate the effectiveness of this proposed framework in capturing the underlying association between different OSNs and achieving superior cold-start recommendation performance.
资助项目	National Natural Science Foundation of China[61432019] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[U1705262] ; National Natural Science Foundation of China[61632004] ; National Natural Science Foundation of China[61632115] ; National Natural Science Foundation of China[61672518] ; National Natural Science Foundation of China[61473030] ; National Natural Science Foundation of China[61332016] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; Beijing Municipal Science and Technology Commission[Z181100008918012]
WOS关键词	SYSTEMS
WOS研究方向	Computer Science ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000450212600021
资助机构	National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Beijing Municipal Science and Technology Commission
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/22614]
专题	多媒体计算与图形学团队
通讯作者	Sang, Jitao
作者单位	1.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 2.Beijing Jiaotong Univ, Beijing Key Lab Traff Data Anal & Min, Beijing 100044, Peoples R China 3.Alibaba, Hangzhou 311121, Zhejiang, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Sang, Jitao,Yan, Ming,Xu, Changsheng. Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(12):3439-3451.
APA	Sang, Jitao,Yan, Ming,&Xu, Changsheng.(2018).Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation.IEEE TRANSACTIONS ON MULTIMEDIA,20(12),3439-3451.
MLA	Sang, Jitao,et al."Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation".IEEE TRANSACTIONS ON MULTIMEDIA 20.12(2018):3439-3451.