AI & Technical question

How does Spotify’s recommendation system work?

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Technical understanding of recommendation systems and the ability to explain one at a level appropriate for a PM interview.

State the goal: predict what a listener wants next, balancing familiarity, discovery and session context.
Describe main signal types: collaborative filtering, audio content features, and playlist co occurrence.
Explain how Spotify likely blends these for Discover Weekly, offline and longer horizon, versus Autoplay, real time and session aware.
Note the cold start problem for new tracks and users, and how content based signals help.
Name evaluation metrics: stream through rate, skip rate, saves, and retention, not just clicks.
Flag that this is informed estimation since exact architecture is not public.

Separates offline personalization from real time session signals like skip behavior.
Explains cold start clearly: new songs rely on audio features and playlist placement before enough data exists.
Uses skip rate and save rate as stronger quality signals than raw plays.

Describing only collaborative filtering without explaining cold start or session level behavior.
Presenting internal Spotify details as fact rather than reasoned estimation.

Chapters of the AI PM course that teach what this question tests.