Netflix

=== Recommendations and thumbnails ===

Netflix presents viewers with recommendations based on previous viewing history and ratings of viewed content. These are often grouped into genres and formats, or feature the platform's highest-rated content. Each title is presented with a thumbnail. Before around 2015, these were the same [[key art]] for everyone, but since then has been customized. Netflix may select a specific actor for a thumbnail based on viewing history,<ref>{{cite web |url=https://www.looper.com/274997/the-secret-behind-netflixs-personalized-thumbnails/ |title=The Secret Behind Netflix's Personalized Thumbnails |author=Dany Roth |date=November 8, 2020}}</ref> or an actor or scene type based on genre preferences.<ref>{{cite web |url=https://netflixtechblog.com/artwork-personalization-c589f074ad76 |title=Artwork Personalization at Netflix |work=Netflix Technology Blog |date=Dec 7, 2017 |author1=Ashok Chandrashekar |author2=Fernando Amat |author3=Justin Basilico |author4=Tony Jebara}}</ref> Some thumbnails are generated from video stills.<ref>[https://blogs.cornell.edu/info2040/2022/09/28/how-netflix-uses-matching-to-pick-the-best-thumbnail-for-you/ How Netflix Uses Matching To Pick The Best Thumbnail For You]</ref>

The Netflix recommendation system is a vital part of the streaming platform's success, enabling personalized content suggestions for over 220 million subscribers worldwide.<ref name="RecoAI">{{cite web |title=Netflix Recommendation System: How it Works |url=https://recoai.net/netflix-recommendation-system-how-it-works/ |work=RecoAI |date=April 5, 2022 |access-date=March 28, 2024}}</ref> Using advanced machine learning algorithms, Netflix analyzes user interactions, including viewing history, searches, and ratings, to deliver personalized recommendations for movies and TV shows.

The recommendation system considers individual user preferences, similarities with other users with comparable tastes, specific title attributes (genre, release year), device usage patterns, and viewing time. As users interact with the platform and provide feedback with their viewing habits, the recommendation system is able to adapt and refine its suggestions over time. Netflix uses a two-tiered ranking system, using the presentation of titles on the homepage for easy navigation to maximize user engagement. This is done by organizing content into rows and ranking the titles within each row based on how much the user would be interested in it.<ref name="RecoAI"/> Netflix also uses A/B testing to determine what causes the biggest interest and engagement related to options concerning movie suggestions and how titles are organized.

Tags like "bittersweet", "sitcom", or "intimate" are assigned to each title by Netflix employees.<ref name="Koblin" /> Netflix also uses the tags to create recommendation micro-genres like "Goofy TV Shows" or "Girls Night In".<ref name="Koblin">{{cite news |newspaper=[[The New York Times]] |author=John Koblin |date=January 14, 2024 |title=A Few Words About Netflix's Success: Vivid. Snappy. Tags. |url=https://www.nytimes.com/2024/01/14/business/media/netflix-streaming-movies-ratings.html}}</ref>