Quantifying Your Taste
A personalization score is the engine of a modern recommendation system. For every user and for every single title in a streaming library containing thousands of options, the platform’s algorithm calculates a predictive score. This is the number that determines the order of virtually everything you see on the screen.
Netflix famously represents this to the user as a ’% Match’ score. A ‘98% Match’ on a film does not mean that 98% of people liked it; it means the algorithm is 98% confident that you specifically will enjoy it based on your data.
How the Score is Calculated
This score is the output of a sophisticated machine learning model that weighs an enormous number of variables to build a detailed ‘taste profile’ for each user. The factors include:
- Genre and Subgenre Affinity: It knows not just that you like ‘comedy,’ but that you specifically like ‘irreverent workplace sitcoms’ or ‘dark comedy features.’
- Actor, Director, and Creator Preferences: It tracks the talent involved in the content you watch.
- Content ‘Tags’: Netflix famously employs human taggers to analyze every film and show and assign it hundreds of highly specific, descriptive metadata tags (e.g., ‘gritty,’ ‘cerebral,’ ‘dystopian,’ ‘ensemble cast’). The algorithm matches these tags against your viewing history.
- Taste Communities: It compares your profile to millions of other users to find people with similar tastes. The score for a new film is heavily influenced by how well it performed with your ‘taste neighbors.’
The ultimate goal of the personalization score is to reduce ‘choice paralysis.’ By ranking all content according to what you are most likely to enjoy, the platform aims to get you to stop browsing and start watching as quickly as possible.