What you're suggesting is that there's a "nonlinear relationship
<http://blog.minitab.com/blog/adventures-in-statistics-2/what-is-the-difference-between-linear-and-nonlinear-equations-in-regression-analysis>"
between the original score (the input variable) and some measure of
"relevance" (the output variable). Nonlinear models like decision trees
(which include LambdaMART) and neural networks (which include RankNet) can
handle these types of situations, assuming there's enough data. The
nonlinear phenomena you brought up are also probably part of the
reason why pairwise
models tend to perform better than pointwise models
<https://www.quora.com/What-are-the-differences-between-pointwise-pairwise-and-listwise-approaches-to-Learning-to-Rank>
in
learning to rank tasks.

On Fri, Jan 12, 2018 at 1:52 PM, Brian Yee <[EMAIL PROTECTED]> wrote: