What's Hot: Linear Popularity Prediction From TV And Social Usage Data (2013)

By Jan Neumann, Xiaodong Yu, and Mohamad Ali Torkamani, Comcast Labs

Large numbers of TV channels are available to TV consumers these days. Such choice is both a blessing and a curse, because too many options can overwhelm the consumer and due to the limited screen real estate on devices, only a small number of programs can be presented at a given time. To address this issue, at Comcast Labs we work on algorithms that compute rankings of current and upcoming programs based on various relevance criteria.

In this paper we describe one of our algorithms, where we predict the future popularity of programs by combining information from historical Nielsen ratings, DVR scheduling activity, and social web activity (e.g. Facebook, Twitter).

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