Let’s look at how content providers can benefit from data-driven strategies to compete in a highly competitive market and maximize their ROI by using machine
In recent years, television services have undergone constant evolution, ceaselessly diversifying their offerings and the formats used to access their content. The distribution of audiovisual
AI powered Recommendation engine: UX Personalization for digital content Video service providers spend a lot of money on expanding their content catalogs, but is it
A good content recommendation system is key for any content provider. Machine Learning video recommendations provide a unique opportunity for broadcasters, Pay-TV operators, TV Networks, and any content distributor to increase engagement and reduce churn through content personalization.
Currently, most recommendation systems operate using explicit information provided by the user about their preferences (for example, by scoring previously watched content) using a technique known as collaborative filtering.
Personalisation is currently a trendy topic and is recognised as one of the key means to improve engagement and reduce churn for video service providers.
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