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The system learns from what people chose. Nobody records what they were looking for and did not find.

Absence produces no signal.

Sufficient consensus

Relevance feedback refines a query from judgements about results, explicitly from the user or implicitly from clicks and dwell. It reliably improves the next round of results.

Implicit feedback is what large systems actually use.

What the result pages leave out

These belong to the subject. People ask about them. They are missing from the agreed coverage.

  • The abandoned search. Someone who gives up and closes the tab has delivered the most important judgement and the least recorded one.
  • Satisfaction that is invisible. A person who reads an answer, closes the page and acts on it looks identical to a person who bounced.
  • Feedback from the wrong person. Aggregated signals mix the expert and the beginner into one average preference that fits neither.
  • The result that was never offered. Feedback can only rank what was shown. A better document that stayed on page four is not part of the conversation.
  • Slow satisfaction. Some answers prove themselves weeks later. No feedback mechanism reaches that far.

What people actually want to know

  • What were people looking for on my site and not finding?
  • Which of my pages ended a search well and looks like a failure in the data?
  • How would I ever learn about the answer I should have written?
  • Who is the average user my numbers describe, and does that person exist?

Ask the people who left. That is the only channel through which the missing answer can reach you.

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