exploration

New users and new items carry zero interaction history, so collaborative filtering has nothing to work with. The candidates who pass treat it as a lifecycle, not a single trick. Here is how.

A recommender trains on data its own past recommendations produced, so it learns to confirm its own beliefs. What matters is spotting the loop, naming the biases it breeds, and knowing the exploration and debiasing fixes that break it.

A track runs three minutes, a session runs an hour, and a fresh release has zero plays on launch day. The interview centers on mixing collaborative filtering with audio content embeddings, handling cold start on both sides, and reading a skip as the loud negative it really is.

This product succeeds or fails on a feedback loop measured in seconds: every swipe is a label, and the following video must respond to it. The tricky part is exploring widely enough to learn what someone enjoys while avoiding a filter bubble or the amplification of junk.