Nowadays, Cable TV operators provide their users multiple ways to watch TV content, such as Live TV and Video on Demand (VOD) services. In the last years, Catch-up TV has been introduced, allowing users to watch recent broadcast content whenever they want to. Understanding how the users interact with such services is important to develop solutions that may increase user satisfaction , user engagement and user consumption. In this paper, we characterize, for the first time, how users interact with a large European Cable TV operator that provides Live TV, Catch-up TV and VOD services. We analyzed many characteristics, such as the service usage, user engagement, program type, program genres and time periods. This characterization will help us to have a deeper understanding on how users interact with these different services, that may be used to enhance the recommendation systems of Cable TV providers.
Some authors performed dataset characterizations that helped in the design of their recommendation systems. @cite_8 presented a high-level dataset characterization and showed that on average more video content is consumed in the weekend. The average number of views for a video decreases exponentially as the time passes. The authors attribute this result to the users viewing behavior and the user interface of the Catch-up TV portal.
@cite_8
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The distribution of videos over the Internet is drastically transforming how media is consumed and monetized. Content providers, such as media outlets and video subscription services, would like to ensure that their videos do not fail, startup quickly, and play without interruptions. In return for their investment in video stream quality, content providers expect less viewer abandonment, more viewer engagement, and a greater fraction of repeat viewers, resulting in greater revenues. The key question for a content provider or a CDN is whether and to what extent changes in video quality can cause changes in viewer behavior. Our work is the first to establish a causal relationship between video quality and viewer behavior, taking a step beyond purely correlational studies. To establish causality, we use Quasi-Experimental Designs, a novel technique adapted from the medical and social sciences. We study the impact of video stream quality on viewer behavior in a scientific data-driven manner by using extensive traces from Akamai's streaming network that include 23 million views from 6.7 million unique viewers. We show that viewers start to abandon a video if it takes more than 2 seconds to start up, with each incremental delay of 1 second resulting in a 5.8 increase in the abandonment rate. Further, we show that a moderate amount of interruptions can decrease the average play time of a viewer by a significant amount. A viewer who experiences a rebuffer delay equal to 1 of the video duration plays 5 less of the video in comparison to a similar viewer who experienced no rebuffering. Finally, we show that a viewer who experienced failure is 2.32 less likely to revisit the same site within a week than a similar viewer who did not experience a failure.