TY - GEN
T1 - vRetention
T2 - 15th ACM Multimedia Systems Conference, MMSys 2024
AU - Chen, Bo Rong
AU - Zhu, Jiayu
AU - Jiang, Yanxin
AU - Hu, Yih Chun
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/4/15
Y1 - 2024/4/15
N2 - Adaptive bitrate streaming (ABR) and quality of experience (QoE) metrics are proposed to enhance video streaming quality across various Internet connections. Traditional approaches to evaluating these metrics often ignore common user behaviors like seeking, jumping, or replaying video segments, leading to gaps in QoE understanding. Addressing this, we collected 229,178 audience retention curves from YouTube and Bilibili, offering a thorough view of viewer engagement and diverse watching styles. Our analysis reveals notable behavioral differences across countries, categories, and platforms. The YouTube data highlights varied content preferences, such as gaming and entertainment in some countries, and music, travel, and pets & animals in others. Additionally, Bilibili shows trends of early video abandonment, possibly influenced by platform-specific factors and shorter video formats. This enhanced grasp of user engagement aids in refining ABR and QoE metrics. We also highlight several potential applications of our dataset.
AB - Adaptive bitrate streaming (ABR) and quality of experience (QoE) metrics are proposed to enhance video streaming quality across various Internet connections. Traditional approaches to evaluating these metrics often ignore common user behaviors like seeking, jumping, or replaying video segments, leading to gaps in QoE understanding. Addressing this, we collected 229,178 audience retention curves from YouTube and Bilibili, offering a thorough view of viewer engagement and diverse watching styles. Our analysis reveals notable behavioral differences across countries, categories, and platforms. The YouTube data highlights varied content preferences, such as gaming and entertainment in some countries, and music, travel, and pets & animals in others. Additionally, Bilibili shows trends of early video abandonment, possibly influenced by platform-specific factors and shorter video formats. This enhanced grasp of user engagement aids in refining ABR and QoE metrics. We also highlight several potential applications of our dataset.
UR - http://www.scopus.com/inward/record.url?scp=85191983860&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191983860&partnerID=8YFLogxK
U2 - 10.1145/3625468.3652175
DO - 10.1145/3625468.3652175
M3 - Conference contribution
AN - SCOPUS:85191983860
T3 - MMSys 2024 - Proceedings of the 2024 ACM Multimedia Systems Conference
SP - 313
EP - 318
BT - MMSys 2024 - Proceedings of the 2024 ACM Multimedia Systems Conference
PB - Association for Computing Machinery
Y2 - 15 April 2024 through 18 April 2024
ER -