Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube

	Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube
	Deng, Zhengyu 1; Yan, Ming1 ; Sang, Jitao1 ; Xu, Changsheng1
发表期刊	ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
	2014-12-01
卷号	11 期号:2
文章类型	Article
摘要	Traditional 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.
关键词	Algorithms Experimentation Performance Short-term Interest Personalization Video Recommendation Time-aware Cross-platform
WOS标题词	Science & Technology ; Technology
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:000348308800008
引用统计	被引频次：18[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/2821
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.China Singapore Inst Digital Media, Singapore, Singapore
第一作者单位	模式识别国家重点实验室
推荐引用方式 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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