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On Migratory Behavior in Video Consumption

Published: 06 November 2017 Publication History

Abstract

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 paper, 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, and discuss the implications of our results to CPs.

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  • (2021)On Migratory Behavior in Video ConsumptionIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304346718:2(1775-1788)Online publication date: Jun-2021
  • (2021)Predicting User Quitting Ratio in Adaptive Bitrate Video StreamingIEEE Transactions on Multimedia10.1109/TMM.2020.304445223(4526-4540)Online publication date: 2021

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    cover image ACM Conferences
    CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management
    November 2017
    2604 pages
    ISBN:9781450349185
    DOI:10.1145/3132847
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    Published: 06 November 2017

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    1. migratory behavior
    2. video consumption

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    • (2021)On Migratory Behavior in Video ConsumptionIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304346718:2(1775-1788)Online publication date: Jun-2021
    • (2021)Predicting User Quitting Ratio in Adaptive Bitrate Video StreamingIEEE Transactions on Multimedia10.1109/TMM.2020.304445223(4526-4540)Online publication date: 2021

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