In search, position of the search result affects the click-through rate a great deal. How do people usually deal with this ? In practice how to remove such bias to create unbiased training data for training learning to rank model ?
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[Position Bias Estimation for Unbiased Learning to Rank in Personal Search](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/46485.pdf) (pdf) – Emre Aug 11 '18 at 01:23
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What are your output (prediction) and inputs?
The bias in itself is very important in predicting the click-through rate, but definitely less useful if you want to assess your ad copy/ page title in relation to the click-through rate.
One potential way to do that is to model the impact of the position of the ad / (or page, if it's for SEO), and try to normalize the data either by including the impact factor in your model or adjusting the result by the impact factor.
I would suspect it would be a log function, as the number of clicks tend to drop significantly after the top 1/2 of the first page.
The Lyrist
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Discounted cumulative gain (DCG) is a common way to measure the quality of search results by measuring usefulness, or gain, of a document based on its position in the result list.
Brian Spiering
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