A YouTube Dataset with User-level Usage Data: Baseline Characteristics and Key Insights

ICC 2020 - 2020 IEEE International Conference on Communications (ICC) Pub Date : 2020-06-01 DOI:10.1109/icc40277.2020.9148782

Shruti Lall, Mohit Agarwal, Raghupathy Sivakumar

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引用次数: 3

Abstract

YouTube is the most popular video sharing platform with more than 2 billion active users and 1 billion hours of video content watched daily. The dominance of YouTube has had a big impact on the performance of Internet protocols, algorithms, and systems. Understanding the interaction of users with YouTube is thus of much interest to the research community. In this context, we collect YouTube watch history data from 243 users spanning a 1.5 year period. The dataset comprises of a total of 1.8 million videos. We use the dataset to analyze and present key insights about user-level usage behavior. We also show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. We present baseline characteristics and also substantiated directions to solve a few representative problems related to local caching techniques, prefetching strategies, the performance of YouTube’s recommendation engine, the variability of user’s video preferences and application specific load provisioning.

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具有用户级使用数据的YouTube数据集:基线特征和关键见解

YouTube是最受欢迎的视频分享平台，拥有超过20亿的活跃用户和每天10亿小时的视频内容观看量。YouTube的主导地位对互联网协议、算法和系统的性能产生了重大影响。因此，了解用户与YouTube的互动是研究界非常感兴趣的。在这种情况下，我们收集了243名用户在1.5年期间的YouTube观看历史数据。该数据集共包含180万个视频。我们使用数据集来分析和呈现关于用户级使用行为的关键见解。我们还表明，我们的分析可以被研究人员用来解决网络和通信一般领域的无数问题。我们提出了基本特征，并提出了解决与本地缓存技术、预取策略、YouTube推荐引擎的性能、用户视频偏好的可变性和应用程序特定负载配置相关的一些代表性问题的具体方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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