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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Web-based catch-up TV has revolutionised watching habits as it provides users the opportunity to watch programs at their preferred time and place, using a variety of devices. With the increasing offer of TV content, there is an emergent need for personalised recommendation solutions, which help users to select programs of interest. In this work, we study the watching patterns of users of an Australian nation-wide catch-up TV service provider and develop a suite of approaches for a catch-up recommendation scenario. We evaluate these approaches using a new large-scale dataset gathered by the Web-based catch-up portal deployed by the provider. The evaluation allows us to compare the performance of several recommenders that address the discovery of both TV programs already watched by users and new programs that users may find relevant. IPTV services deployed nowadays often consist of both live TV and Video-on-Demand (VoD), offered by the same service provider to the same pool of users over the same managed network. Understanding user behaviors in such a setting is hence an important step for system modelling and optimization. Previous studies on user behavior on video services were on either live TV or VoD. For the first time, we conduct an in-depth large-scale behavior study for IPTV users offering simultaneously live TV and VoD choices at the same time. Our data is from the largest IPTV service provider in China, offering hundreds of live channels and hundreds of thousands of VoD files, with traces covering more than 1.9 million users over a period of 5 months. This large dataset provides us a unique opportunity to cross-compare user viewing behaviors for these services on the same platform, and sheds valuable insights on how users interact with such a simultaneous system. Our results lead to new understanding on IPTV user behaviors which have strong implications on system design. For example, we find that the average holding time for VoD is significantly longer than live TV. live TV users tend to surf more. However, if such channel surfing is discounted, the holding times of both services are not much different. While users in VoD tend to view HD longer, channel popularity for live TV is much less dependent on its video quality. In contrast to some popular assumptions on user interactivity, the transitions among live TV, VoD, and offline modes are far from a Markov model. As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events. We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1 increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content. Predicting Web content popularity is an important task for supporting the design and evaluation of a wide range of systems, from targeted advertising to effective search and recommendation services. We here present two simple models for predicting the future popularity of Web content based on historical information given by early popularity measures. Our approach is validated on datasets consisting of videos from the widely used YouTube video-sharing portal. Our experimental results show that, compared to a state-of-the-art baseline model, our proposed models lead to significant decreases in relative squared errors, reaching up to 20 reduction on average, and larger reductions (of up to 71 ) for videos that experience a high peak in popularity in their early days followed by a sharp decrease in popularity.