Abstract
IPTV has been widely deployed throughout the world, bringing significant advantages to users in terms of the channel offering, video on demand, and interactive applications. One aspect that has been often neglected is the ability of precise and unobtrusive telemetry. TV set-top boxes that are deployed in modern IPTV systems can be thought of as capable sensor nodes that collect vast amounts of data, representing both the user activity and the quality of service delivered by the system itself. In this paper we focus on the user-generated events and analyze how the data stream of channel change events received from the entire IPTV network can be mined to obtain insight about the content. We demonstrate that it is possible to predict the occurrence of TV ads with high probability and show that the approach could be extended to model the user behavior and classify the viewership in multiple dimensions.
Highlights
Internet Protocol Television (IPTV) has become an essential part of modern triple-play offerings and is being widely deployed worldwide
Based on the hypothesis that TV ad segments represent undesired content to most viewers, we develop and validate an algorithm to detect ad occurrences based on the number of synchronous channel change events on the TV channel
The Receiver Operating Characteristic (ROC) curve [10] is a widely used tool whose plot represents the compromise between the true positive and the false positive example classifications based on a continuous output along all its possible decision threshold values
Summary
Internet Protocol Television (IPTV) has become an essential part of modern triple-play offerings and is being widely deployed worldwide. One of the available data streams in such networks are the channel change events, which can be obtained in a multitude of ways: either reported by the STB diagnostics module, acquired through the IPTV middleware, or captured directly from the network elements of the core or access delivery network. Such data, despite its simplicity, hides a wealth of information about the user habits and about the content itself, since each channel change event is motivated by a combination of the viewer’s habits and context. Based on the hypothesis that TV ad segments represent undesired content to most viewers, we develop and validate an algorithm to detect ad occurrences based on the number of synchronous channel change events on the TV channel
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