Dating App Algorithms: Matching Logic and Engagement

The ELO Score

Early apps ranked users by "desirability". If a popular person swipes right on you, your score goes up. Modern apps use vectors of taste.

Users say they want "Kind, 6ft tall". They swipe on "Hot, Bad Boy". Algorithms trust behavior (swipes) over stated preferences.

Use Geohash or S2 cells for efficient "people near me" queries. Calculate radius search on grid cells, not raw coords.

A perfect match means two users leave the app (churn). Apps must balance successful matches with keeping users swiping.

New users need exposure to get a "score". Boost new profiles temporarily to gather data and hook them with matches.

Detect bots early. Verify photos with selfie-challenges (pose matching). Block device IDs of banned predators.

Super Likes and Boosts work because they bypass the black-box algorithm. Selling "visibility" is the core business model.