2015年5月12日学术报告信息（Simon Fraser University:Jiangchuan Liu ）

2015年5月12日学术报告信息（Simon Fraser University:Jiangchuan Liu ）

类别：网络整理发布人：admin 浏览次数：次发布时间：2015-09-02 14:25

报告题目：Videos in Social Media: Generation, Distribution, and Prediction

报告日期及时间：2015年5月12日上午10:00-11:00

报告地点：计算机学院4楼B403报告厅

报告人： Jiangchuan Liu

报告人单位：Simon Fraser University, Canada

报告人简介：
Jiangchuan Liu (S1-M3-SM8) is a Full Professor in the Schoolof Computing Science, Simon Fraser University, British Columbia, Canada, and an NSERC E.W.R. Steacie Memorial Fellow. He is an EMC-Endowed Visiting Chair Professor of Tsinghua University, Beijing, China (2013-2016). From 2003 to 2004, he was an Assistant Professor at The Chinese University of Hong Kong.
He received the BEng degree (cum laude) from Tsinghua University,Beijing, China, in 1999, and the PhD degree from The
Hong KongUniversity of Science and Technology in 2003, both in computerscience. He is a co-recipient of the inaugural Test of Time Paper Award of IEEE INFOCOM (for the CoolStreaming paper publishedin 2005, which has been cited 2035 times
till year 2015), ACM TOMCCAP Nicolas D. GeorganasBest Paper Award (2013), ACM Multimedia Best Paper Award (2012),
IEEE Globecom Best Paper Award (2011), and IEEE Communications SocietyBest Paper Award on Multimedia
Communications (2009). His studentsreceived the Best Student Paper Award of IEEE/ACM IWQoS twice (2008 and 2012).
His research interests include multimedia systems and networks,cloud computing, social networking, online gaming, big
datacomputing, wireless sensor networks, and peer-to peer and overlaynetworks. He has served on the editorial boards of IEEE Transactions on Big Data, Transactions on Multimedia, IEEE Communications Surveys and Tutorials, IEEE Access, IEEE Internet of Things Journal, Computer Communications, and Wiley Wireless Communications andMobile
Computing. He is the Steering Committee Chair of IEEE/ACMIWQoS from 2013 to 2017. His papers have been cited around
9000 times, and his H-index is 42.

报告摘要：
In this talk, I will overview the recent research in my lab on big data computing, crowdsourcing, social networking, online gaming,
structure health monitoring, and etc.
I will then present a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. SocialForecast explicitly considers the dynamically changing and evolving
propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training
phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its
short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.

邀请人：王骞教授王志波副教授

上一篇：5月8日学术报告(University of Tennessee, Knoxville: Wei Gao)

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