A recommender ranks candidates, not truth
Explain why ranking position should not be treated as truth or importance.
The move: separate rank from truth. A recommender system solves an overload problem. It cannot show everything, so it retrieves candidates, scores them, and may re-rank them with constraints such as freshness or diversity. The output is a useful ordered list, but the order reflects the system objective and available signals. That objective may be relevance, predicted engagement, similarity to past behavior, similarity to users like you, business priority, or a blend. None of those is identical to accuracy. A high-ranked post can be true and important. It can also be incomplete, emotionally sticky, or simply close to what you…
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