Explain and Reduce Position Bias in Ranking

Describe position bias in a click-trained ranking system. In what way can the display position of an item skew the observed relevance signal, and how would you detect and correct that skew? Outline a specific modeling method, the data it needs, and how you would judge whether a new ranker actually improves relevance instead of merely mimicking the prior ordering.

What a Strong Answer Covers

• The difference between an item being examined, being relevant, and being clicked.
• Why raw click-through rates broken down by position mix up presentation effects with the item choices made by the current ranker.
• A reasonable method for estimating position effects and applying those estimates during training, along with its assumptions and where it breaks down.
• An evaluation approach that does not label every unclicked or never-shown item as irrelevant.

Follow-up Questions

• What constraints appear when the logging system always places the same items in the same positions?
• How do very small examination propensities affect learning or offline evaluation?

Overview: Describe position bias in click-based ranking, separate examination from relevance, and evaluate propensity estimation, debiasing, and trustworthy evaluation.