How Video Recommendation Works

flipps imgWhat is Video Recommendation

Through the process of video recommendation, we get suggested video content, based on our user profile and interests. Video recommendation is not a simple task, but a whole set of algorithms, that facilitate our quality viewing experience.

Video streaming platforms usually provide advanced tools for easy discovery of content from the moment we first interact with the application. The video recommendation engine collects data from multiple sources to build a complete user profile.

Video-based Methods for Building Recommendations

Recommendation engines aggregate video-based data to provide quality suggestions. Some methods include creating a Related Videos List, generated for each video. Every video interrelated with a ‘bucket’ of similar videos.

Another advanced method for enhancing video recommendations is the integration with machine learning algorithms. A popular machine learning algorithm used in sophisticated video streaming platforms is based on Apache Mahout.

Applying video-based methods facilitates useful recommendations, where the viewer gets suggestions, based on related videos from the same ‘bucket’ only. Based on techniques like content similarity, the engines build highly relevant content lists depending on the context in which we are viewing and the type of content we are browsing.

User-based Methods for Building Complete User Profile

User-based methods for building complete user profiles include sources like demographics data and personal interests from Facebook, a clear choice of attention during the onboarding process. Behavioral data points, like the viewed type of content (news, movies, series, clips, live events, etc.) and video genres seen, provide another essential flow of data to video recommendation engines.

Collected data is then used as input for building personal recommendations lists organized by topics, genres, what other users watched, etc.

User-based video content targeting includes techniques like collaborative filtering based on similar users’ sentiments and content-based filtering from selected videos (the last watched; top search results for user’s related interests).
User similarity is usually defined, based on machine learning algorithms and user profile properties matching.

Video Recommendations Across Screens and Formats

Bianor’s deep expertise in building and delivering video streaming platforms successful due to the reliability and effectiveness of the video recommendation engines we provide as part of our solutions. The audience receives a rich viewing experience across screens and formats – from native mobile video to the lean-back experience of the large TV screen.

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