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 Related Videos List, which is calculated for each video. Every video is 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 effective recommendations, where the viewer gets suggestions, based on related videos, which are 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, explicit choice of interests during onboarding process. Behavioral data points, like watched type of content (news, movies, series, clips, live events, etc.) and what videos are watched from which genres, provide another important flow of data to video recommendation engines.

Collected data then is 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 sentiments of similar users and content-based filtering from selected videos (last N 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 is successful due to the reliability and effectiveness of the video recommendation engines we deliver 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.