Methods of machine learning for linear TV recommendations M. A. Baklanov, O. E. Baklanova
Material type:
ArticleSubject(s): машинное обучение | линейное телевидение | умное телевидение | Smart TV | рекомендательные системы | рекомендации контента | видеоконтентGenre/Form: статьи в сборниках Online resources: Click here to access online
In:
Intelligent computing methodologies : 12th international conference, ICIC 2016, Lanzhou, China, August 2-5, 2016 : proceedings Lecture notes in computer science ; vol. 9773 . Pt. 3. P. 607-615Abstract: This paper describes methods of improving TV-watching experience using Machine Learning for Linear TV recommendations. There is an overview of existing methods for video content recommendations and an attempt of developing new method that focused only on linear TV recommendations and takes into account all specifics around it. Recommendation system based on this approach was implemented in Russian pay TV provider ZOOM TV, and demonstrated two times churn rate reduction in comparison with same service without recommendation system. Existing methods and new method effectiveness compared with offered approach by analyzing real people content consumption during 1 year.
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This paper describes methods of improving TV-watching experience using Machine Learning for Linear TV recommendations. There is an overview of existing methods for video content recommendations and an attempt of developing new method that focused only on linear TV recommendations and takes into account all specifics around it. Recommendation system based on this approach was implemented in Russian pay TV provider ZOOM TV, and demonstrated two times churn rate reduction in comparison with same service without recommendation system. Existing methods and new method effectiveness compared with offered approach by analyzing real people content consumption during 1 year.
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