CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube
Deng, Zhengyu1; Yan, Ming1; Sang, Jitao1; Xu, Changsheng1
Source PublicationACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
2014-12-01
Volume11Issue:2
SubtypeArticle
AbstractTraditional personalized video recommendation methods focus on utilizing user profile or user history behaviors to model user interests, which follows a static strategy and fails to capture the swift shift of the short-term interests of users. According to our cross-platform data analysis, the information emergence and propagation is faster in social textual stream-based platforms than that in multimedia sharing platforms at micro user level. Inspired by this, we propose a dynamic user modeling strategy to tackle personalized video recommendation issues in the multimedia sharing platform YouTube, by transferring knowledge from the social textual stream-based platform Twitter. In particular, the cross-platform video recommendation strategy is divided into two steps. (1) Real-time hot topic detection: the hot topics that users are currently following are extracted from users' tweets, which are utilized to obtain the related videos in YouTube. (2) Time-aware video recommendation: for the target user in YouTube, the obtained videos are ranked by considering the user profile in YouTube, time factor, and quality factor to generate the final recommendation list. In this way, the short-term (hot topics) and long-term (user profile) interests of users are jointly considered. Carefully designed experiments have demonstrated the advantages of the proposed method.
KeywordAlgorithms Experimentation Performance Short-term Interest Personalization Video Recommendation Time-aware Cross-platform
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS IDWOS:000348308800008
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/2821
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.China Singapore Inst Digital Media, Singapore, Singapore
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Deng, Zhengyu,Yan, Ming,Sang, Jitao,et al. Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2014,11(2).
APA Deng, Zhengyu,Yan, Ming,Sang, Jitao,&Xu, Changsheng.(2014).Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,11(2).
MLA Deng, Zhengyu,et al."Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 11.2(2014).
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