EP-25 How Deep Learning Based Recommendation System works in OTT Platforms?

In this episode I narrate the story of recommendation system used by OTT Platforms, Social Medias and Video Platforms like Youtube Recommendation System Item Based Filtering Choice Based Recommendation Netflix Recommendation Engine Content Based Filtering Collabaritive Filtering How does OTT recommendation work? Custom recommendation system analyzes the past data history of a user and predicts the future insights that are more likely to engage the user. Cloud-based recommender systems help the OTT or VOD service providers in better understanding whether a service satisfies the user requirements or not. Which recommendation system next flix use? The Netflix Recommendation Engine Their most successful algorithm, Netflix Recommendation Engine (NRE), is made up of algorithms which filter content based on each individual user profile. The engine filters over 3,000 titles at a time using 1,300 recommendation clusters based on user preferences. What is  Content-based filtering? Content-based filtering uses item features to recommend other items similar to what the user likes, based on their previous actions or explicit feedback Do check the episode on various platform  https://www.instagram.com/podcasteramit Apple :https://podcasts.apple.com/us/podcast/id1544510362 Huhopper Platform :https://hubhopper.com/podcast/tech-stories/318515 Amazon: https://music.amazon.com/podcasts/2fdb5c45-2016-459e-ba6a-3cbae5a1fa4d Spotify :https://open.spotify.com/show/2GhCrAjQuVMFYBq8GbLbwa