Abstract
This paper presents Pop-Forecast, a systematic method for accurately forecasting the popularity of videos promoted through social networks. Pop-Forecast aims to optimize the forecasting accuracy and the timeliness with which forecasts are issued, by explicitly taking into account the dynamic propagation of videos in social networks. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the performance loss of Pop-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 fast rate of convergence as well as its asymptotic convergence to the optimal performance. We validate the performance of Pop-Forecast through 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 approaches for popularity prediction (which do not take into account the propagation in social network) by more than 30% in terms of prediction rewards.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE INFOCOM |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2308-2316 |
| Number of pages | 9 |
| Volume | 26 |
| ISBN (Print) | 9781479983810 |
| DOIs | |
| State | Published - Aug 21 2015 |
| Externally published | Yes |
| Event | 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 - Hong Kong, Hong Kong Duration: Apr 26 2015 → May 1 2015 |
Other
| Other | 34th IEEE Annual Conference on Computer Communications and Networks, IEEE INFOCOM 2015 |
|---|---|
| Country | Hong Kong |
| City | Hong Kong |
| Period | 4/26/15 → 5/1/15 |
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ASJC Scopus subject areas
- Computer Science(all)
- Electrical and Electronic Engineering
Cite this
Timely video popularity forecasting based on social networks. / Xu, Jie; Van Der Schaar, Mihaela; Liu, Jiangchuan; Li, Haitao.
Proceedings - IEEE INFOCOM. Vol. 26 Institute of Electrical and Electronics Engineers Inc., 2015. p. 2308-2316 7218618.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Timely video popularity forecasting based on social networks
AU - Xu, Jie
AU - Van Der Schaar, Mihaela
AU - Liu, Jiangchuan
AU - Li, Haitao
PY - 2015/8/21
Y1 - 2015/8/21
N2 - This paper presents Pop-Forecast, a systematic method for accurately forecasting the popularity of videos promoted through social networks. Pop-Forecast aims to optimize the forecasting accuracy and the timeliness with which forecasts are issued, by explicitly taking into account the dynamic propagation of videos in social networks. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the performance loss of Pop-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 fast rate of convergence as well as its asymptotic convergence to the optimal performance. We validate the performance of Pop-Forecast through 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 approaches for popularity prediction (which do not take into account the propagation in social network) by more than 30% in terms of prediction rewards.
AB - This paper presents Pop-Forecast, a systematic method for accurately forecasting the popularity of videos promoted through social networks. Pop-Forecast aims to optimize the forecasting accuracy and the timeliness with which forecasts are issued, by explicitly taking into account the dynamic propagation of videos in social networks. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the performance loss of Pop-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 fast rate of convergence as well as its asymptotic convergence to the optimal performance. We validate the performance of Pop-Forecast through 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 approaches for popularity prediction (which do not take into account the propagation in social network) by more than 30% in terms of prediction rewards.
UR - http://www.scopus.com/inward/record.url?scp=84954230168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84954230168&partnerID=8YFLogxK
U2 - 10.1109/INFOCOM.2015.7218618
DO - 10.1109/INFOCOM.2015.7218618
M3 - Conference contribution
AN - SCOPUS:84954230168
SN - 9781479983810
VL - 26
SP - 2308
EP - 2316
BT - Proceedings - IEEE INFOCOM
PB - Institute of Electrical and Electronics Engineers Inc.
ER -