feedback loops

When labels exist only for the cases you already acted on, the model learns a distorted world: strong offline, blind to everyone you never saw. Worse, its own decisions choose the next labels. Here is how to spot it and counter it.

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.

Production usage can become your best source of training data, or a self-reinforcing trap. What matters is knowing how to capture clean feedback and how to break the loops that quietly corrupt the model.