TY - JOUR
T1 - On Migratory Behavior in Video Consumption
AU - Yan, Huan
AU - Fu, Haohao
AU - Li, Yong
AU - Lin, Tzu Heng
AU - Wang, Gang
AU - Zheng, Haitao
AU - Jin, Depeng
AU - Zhao, Ben Y.
N1 - Funding Information:
Manuscript received September 2, 2020; revised November 16, 2020; accepted November 19, 2020. Date of publication December 9, 2020; date of current version June 10, 2021. This work was supported in part by Beijing Natural Science Foundation under Grant L182038; in part by the National Key Research and Development Program of China under Grant 2018YFB1800804; in part by the National Natural Science Foundation of China under Grant U1936217, Grant 61971267, Grant 61972223, Grant 61941117, and Grant 61861136003; and in part by the Research Fund of Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology. The associate editor coordinating the review of this article and approving it for publication was P. Calyam. (Corresponding author: Yong Li.) Huan Yan, Yong Li, Tzu-Heng Lin, and Depeng Jin are with the Beijing National Research Center for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (e-mail: liyong07@tsinghua.edu.cn).
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - Today's video streaming market is crowded with various content providers (CPs). For individual CPs, understanding user behavior, in particular how users migrate among different CPs, is crucial for improving users' on-site experience and the CP's chance of success. In this article, we take a data-driven approach to analyze and model user migration behavior in video streaming, i.e., users switching content provider during active sessions. Based on a large ISP dataset over two months (6 major content providers, 3.8 million users, and 315 million video requests), we study common migration patterns and reasons of migration. We find that migratory behavior is prevalent: 66% of users switch CPs with an average switching frequency of 13%. In addition, migration behaviors are highly diverse: regardless large or small CPs, they all have dedicated groups of users who like to switch to them for certain types of videos. Regarding reasons of migration, we find CP service quality rarely causes migration, while a few popular videos play a bigger role. Nearly 60% of cross-site migrations are landed to 0.14% top videos. Finally, we validate our findings by building an accurate regression model to predict user migration frequency as well as user survey, and discuss the implications of our results to CPs.
AB - Today's video streaming market is crowded with various content providers (CPs). For individual CPs, understanding user behavior, in particular how users migrate among different CPs, is crucial for improving users' on-site experience and the CP's chance of success. In this article, we take a data-driven approach to analyze and model user migration behavior in video streaming, i.e., users switching content provider during active sessions. Based on a large ISP dataset over two months (6 major content providers, 3.8 million users, and 315 million video requests), we study common migration patterns and reasons of migration. We find that migratory behavior is prevalent: 66% of users switch CPs with an average switching frequency of 13%. In addition, migration behaviors are highly diverse: regardless large or small CPs, they all have dedicated groups of users who like to switch to them for certain types of videos. Regarding reasons of migration, we find CP service quality rarely causes migration, while a few popular videos play a bigger role. Nearly 60% of cross-site migrations are landed to 0.14% top videos. Finally, we validate our findings by building an accurate regression model to predict user migration frequency as well as user survey, and discuss the implications of our results to CPs.
KW - Video consumption
KW - migratory behavior
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U2 - 10.1109/TNSM.2020.3043467
DO - 10.1109/TNSM.2020.3043467
M3 - Article
AN - SCOPUS:85097954678
SN - 1932-4537
VL - 18
SP - 1775
EP - 1788
JO - IEEE Transactions on Network and Service Management
JF - IEEE Transactions on Network and Service Management
IS - 2
M1 - 9288838
ER -