Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube
文献类型：期刊论文
| 作者 | Deng, Zhengyu1; Yan, Ming1 ; Sang, Jitao1; Xu, Changsheng1
|
| 刊名 | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
 |
| 出版日期 | 2014-12-01 |
| 卷号 | 11期号:2 |
| 关键词 | Algorithms Experimentation Performance Short-term interest personalization video recommendation time-aware cross-platform |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods |
| 研究领域[WOS] | Computer Science |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000348308800008 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2821]  |
| 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.China Singapore Inst Digital Media, Singapore, Singapore |
| 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). |
入库方式： OAI收割
来源：自动化研究所
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