CASIA OpenIR  > 多媒体计算与图形学团队
Understanding Dynamic Cross-OSN Associations for Cold-Start Recommendation
Sang, Jitao1,2; Yan, Ming3; Xu, Changsheng4
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
ISSN1520-9210
2018-12-01
Volume20Issue:12Pages:3439-3451
Corresponding AuthorSang, Jitao(jtsang@bjtu.edu.cn)
AbstractOnline 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.
KeywordCross-OSN association dynamic user modeling cold-start recommendation
DOI10.1109/TMM.2018.2839530
WOS KeywordSYSTEMS
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Beijing Municipal Science and Technology Commission
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000450212600021
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22615
Collection多媒体计算与图形学团队
Corresponding AuthorSang, Jitao
Affiliation1.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
Recommended Citation
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.
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