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A relative preference model that reframes clickthrough data as comparative judgments between examined results rather than absolute relevance signals, using eye-tracking and controlled experiments to calibrate interpretation of user clicks. Identifies that absolute click rates carry strong presentation bias (position, snippet quality), but relative click patterns - specifically "clicked above non-clicked" pairs - yield reliable relevance signals robust to trust bias and ranking artefacts. Enables search systems to extract high-quality implicit feedback for learning-to-rank algorithms by mining pairwise preference constraints from click logs rather than treating raw click frequency as a direct relevance proxy.