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Keynote | Business

Video recommendations and Machine Learning

Thursday 16th | 18:00 - 18:30 | Theatre 20


One-liner summary:

A good content recommendation system is key for any video 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.

Keywords defining the session:

- Video

- Machine learning

- Recommendations

Description:

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. Most recommendation systems may also factor in a user’s profile when suggesting appropriate content, and in doing so may use the preference and feedback information provided by similar users. Such user profile information may contain demographic and geographic data, in addition to more dynamic data, such as the user’s web activity (e.g. pages visited, videos watched, activity on social networks). Recommendation engines also use techniques that are based on the similarity of pieces of content, data that is used to make recommendations based on previously watched content. Video Recommendation Technology Today’s video recommendation technology uses machine learning to train, predict and provide video recommendations to video service users. For the most part, it uses algorithms to identify item similarity complemented with the user’s view history to produce a recommendation. Item similarity: Users who liked this might also like… If a user shows interest in specific content, similar content can be recommended via a non-personalised but effective recommendation algorithm known as a content-based-type recommendation. The basic algorithm works as follows: – To measure how similar two given items of content are, a feature vector, which encodes different scored metadata (E.g. genre information) should be assigned to each content and should compute the angle between each pair of vectors in Euclidean space. The smaller the angle between the vectors, the more similar the content is. – Given an item of content, a shortlist of recommended similar items is produced by finding the most popular and best-rated examples among the most similar content.