  The purpose of modeling document relevance for search engines is to rank
better in subsequent searches. Document-specific historical click-through rates
can be important features in a dynamic ranking system which updates as we
accumulate more sample. This paper describes the properties of several such
features, and tests them in controlled experiments. Extending the inverse
propensity weighting method to documents creates an unbiased estimate of
document relevance. This feature can approximate relevance accurately, leading
to near-optimal ranking in ideal circumstances. However, it has high variance
that is increasing with respect to the degree of position bias. Furthermore,
inaccurate position bias estimation leads to poor performance. Under several
scenarios this feature can perform worse than biased click-through rates. This
paper underscores the need for accurate position bias estimation, and is unique
in suggesting simultaneous use of biased and unbiased position bias features.
