Abstract
Understanding streaming user behavior is crucial to the design of large-scale Video-on-Demand (VoD) systems. In this article, we begin with the measurement of individual viewing behavior from two aspects: the temporal characteristics and user interest. We observe that active users spend more hours on each active day, and their daily request time distribution is more scattered than that of the less active users, while the inter-view time distribution differs negligibly between two groups. The common interest in popular videos and the latest uploaded videos is observed in both groups. We then investigate the predictability of video popularity as a collective user behavior through early views. In the light of the limitations of classical approaches, the Autoregressive-Moving-Average (ARMA) model is employed to forecast the popularity dynamics of individual videos at fine-grained time scales, thus achieving much higher prediction accuracy. When applied to video caching, the ARMA-assisted Least Frequently Used (LFU) algorithm can outperform the Least Recently Used (LRU) by 11--16%, the well-tuned LFU by 6--13%, and the LFU is only 2--4% inferior to the offline LFU in terms of hit ratio.
- Armin Bunde, Jan F. Eichner, Jan W. Kantelhardt, and Shlomo Havlin. 2005. Long-term memory: A natural mechanism for the clustering of extreme events and anomalous residual times in climate records. Physical Review Letters 94, 4 (February 2005), 048701.Google Scholar
Cross Ref
- Shi Min Cai, Zhong Qian Fu, Tao Zhou, Jun Gu, and Pei Ling Zhou. 2009. Scaling and memory in recurrence intervals of Internet traffic. EPL (Europhysics Letters) 87, 6 (2009), 68001.Google Scholar
Cross Ref
- Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, and Sue Moon. 2007. I tube, you tube, everybody tubes: Analyzing the world’s largest user generated content video system. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC’07). ACM, New York, 1--14. Google Scholar
Digital Library
- Meeyoung Cha, Haewoon Kwak, Pablo Rodriguez, Yong-Yeol Ahn, and Sue Moon. 2009. Analyzing the video popularity characteristics of large-scale user generated content systems. IEEE/ACM Transactions on Networking 17, 5 (Oct 2009), 1357--1370. Google Scholar
Digital Library
- Gloria Chatzopoulou, Cheng Sheng, and Michalis Faloutsos. A first step towards understanding popularity in youtube. In Proceedings of the INFOCOM IEEE Conference on Computer Communications Workshops. 1--6.Google Scholar
- Liang Chen, Yipeng Zhou, and Dah Ming Chiu. 2013. Video browsing-a study of user behavior in online VoD services. In Proceedings of the IEEE 2013 22nd International Conference on Computer Communications and Networks (ICCCN). IEEE, 1--7.Google Scholar
Cross Ref
- Cisco 2017. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016-2021 White Paper. Cisco. Retrieved from http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html.Google Scholar
- Kwang-Il Goh and Albert-László Barabási. 2008. Burstiness and memory in complex systems. EPL (Europhysics Letters) 81, 4 (2008), 48002.Google Scholar
Cross Ref
- Lei Huang, Bowen Ding, Yuedong Xu, and Yipeng Zhou. 2017. Analysis of user behavior in a large-scale VoD system. In Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’17). ACM, 49--54. Google Scholar
Digital Library
- Zhenyu Li, Gaogang Xie, Mohamed Ali Kaafar, and Kave Salamatian. 2015. User behavior characterization of a large-scale mobile live streaming system (WWW’15 Companion). ACM, New York, 307--313. Google Scholar
Digital Library
- Valerie N. Livina, Shlomo Havlin, and Armin Bunde. 2005. Memory in the occurrence of earthquakes. Physical Review Letters 95, 20 (November 2005), 208501.Google Scholar
Cross Ref
- Henrique Pinto, Jussara M. Almeida, and Marcos A. Gonçalves. 2013. Using early view patterns to predict the popularity of YouTube videos. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining (WSDM’13). ACM, New York, 365--374. Google Scholar
Digital Library
- Reza Rawassizadeh, Elaheh Momeni, Chelsea Dobbins, Joobin Gharibshah, and Michael Pazzani. 2016. Scalable daily human behavioral pattern mining from multivariate temporal data. IEEE Transactions on Knowledge and Data Engineering 28, 11 (2016), 3098--3112. Google Scholar
Digital Library
- Suman Deb Roy, Tao Mei, Wenjun Zeng, and Shipeng Li. 2013. Towards cross-domain learning for social video popularity prediction. IEEE Transactions on Multimedia 15, 6 (2013), 1255--1267. Google Scholar
Digital Library
- Maxim Shcherbakov, Adriaan Brebels, N. L. Shcherbakova, A. P. Tyukov, T. A. Janovsky, and V. A. Kamaev. 2013. A survey of forecast error measures. World Applied Sciences Journal 24 (2013), 171--176.Google Scholar
- Gabor Szabo and Bernardo A. Huberman. 2010. Predicting the popularity of online content. Communications of the ACM 53, 8 (2010), 80--88. Google Scholar
Digital Library
- Stefano Traverso, Mohamed Ahmed, Michele Garetto, Paolo Giaccone, Emilio Leonardi, and Saverio Niccolini. 2015. Unravelling the impact of temporal and geographical locality in content caching systems. IEEE Transactions on Multimedia 17, 10 (2015), 1839--1854.Google Scholar
Digital Library
- Tomasz Trzciński and Przemysław Rokita. 2017. Predicting popularity of online videos using support vector regression. IEEE Transactions on Multimedia 19, 11 (Nov 2017), 2561--2570.Google Scholar
Cross Ref
- Wikimapia. 2017. Entropy (information theory). Wikimapia. Retrieved from https://en.wikipedia.org/wiki/Entropy_(information_theory).Google Scholar
- Changqiao Xu, Shijie Jia, Mu Wang, Lujie Zhong, Hongke Zhang, and Gabriel-Miro Muntean. 2015. Performance-aware mobile community-based VoD streaming over vehicular ad hoc networks. IEEE Transactions on Vehicular Technology 64, 3 (March 2015), 1201--1217.Google Scholar
Cross Ref
- Changqiao Xu, Shijie Jia, Lujie Zhong, Hongke Zhang, and Gabriel-Miro Muntean. 2014. Ant-inspired mini-community-based solution for video-on-demand services in wireless mobile networks. IEEE Transactions on Broadcasting 60, 2 (June 2014), 322--335.Google Scholar
Cross Ref
- Yuedong Xu, Zhujun Xiao, Hui Feng, Tao Yang, Bo Hu, and Yipeng Zhou. 2017. Modeling buffer starvations of video streaming in cellular networks with large-scale measurement of user behavior. IEEE Transactions on Mobile Computing 16, 8 (2017), 2228--2245.Google Scholar
Digital Library
- Hongliang Yu, Dongdong Zheng, Ben Y. Zhao, and Weimin Zheng. 2006. Understanding user behavior in large-scale video-on-demand systems. ACM SIGOPS Operating Systems Review 40, 4 (2006), 333--344. Google Scholar
Digital Library
- Yan Zhang, Lin Wang, Yi-Qing Zhang, and Xiang Li. 2012. Towards a temporal network analysis of interactive wifi users. EPL (Europhysics Letters) 98, 6 (2012), 68002.Google Scholar
Cross Ref
- Zhi Dan Zhao, Hu Xia, Ming Sheng Shang, and Tao Zhou. 2011. Empirical analysis on the human dynamics of a large-scale short message communication system. Chinese Physics Letters 28, 6 (2011), 068901.Google Scholar
Cross Ref
Index Terms
User Behavior Analysis and Video Popularity Prediction on a Large-Scale VoD System
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