Why is user feedback important in recommendation systems?

Example Answer

Feedback helps a recommender update its understanding, but I would distinguish explicit preferences from ambiguous behavior. A dislike is different from an unseen item, and a skip can mean poor placement, no time or lack of interest. Log impressions, positions and interaction timestamps alongside actions. Immediate profile or eligibility changes can make the next request responsive without retraining the model after every click. I would evaluate changes against satisfaction and quality guardrails rather than assuming more engagement means better recommendations.

Make it your own

Illustrative event pair: an impression with item, position and request ID, followed by an optional click, dismissal or rating. Preserve the absence of exposure separately from non-clicks.

Why this works

Missing feedback is not a complete negative label. Follow-up: how would position bias affect what the model learns from clicks?

Sources: Offline contextual-bandit evaluation research, Google recommendation architecture.