题目: Forecasting Popularity of Videos in Social Media
Abstract: 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. Social-Forecast 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.
Bio: Jiangchuan Liu is a Full Professor in the School of Computing Science, Simon Fraser University, British Columbia, Canada, and an NSERC E.W.R. Steacie Memorial Fellow (NSERC's most prestigious award to young professors within 12 years of PhD graduation). 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 Kong University of Science and Technology in 2003, both in computer science. He is a co-recipient of the inaugural Test of Time Paper Award of IEEE INFOCOM (2015; for the INFOCOM'05 paper on CoolStreaming, which has been cited 2000+ times); ACM TOMCCAP Nicolas D. Georganas Best Paper Award (2013), ACM Multimedia Best Paper Award (2012), IEEE Globecom Best Paper Award (2011), and IEEE Communications Society Best Paper Award on Multimedia Communications (2009). His students received 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 data computing, wireless sensor networks, and peer-to-peer and overlay networks. He has served on the editorial boards of IEEE Transactions on Big Data, IEEE Transactions on Multimedia, IEEE Communications Surveys and Tutorials, IEEE Access, IEEE Internet of Things Journal, Computer Communications, and Wiley Wireless Communications and Mobile Computing. He is the Steering Committee Chair of IEEE/ACM IWQoS from 2015 to 2017. His papers have been cited around 9000 times, and his H-index is 42.