This change adds Expected Reciprocal Rank (ERR) as a ranking evaluation metric
as descriped in:
Chapelle, O., Metlzer, D., Zhang, Y., & Grinspan, P. (2009).
Expected reciprocal rank for graded relevance.
Proceeding of the 18th ACM Conference on Information and Knowledge Management.
https://doi.org/10.1145/1645953.1646033
ERR is an extension of the classical reciprocal rank to the graded relevance
case and assumes a cascade browsing model. It quantifies the usefulness of a
document at rank `i` conditioned on the degree of relevance of the items at ranks
less than `i`. ERR seems to be gain traction as an alternative to (n)DCG, so it
seems like a good metric to support. Also ERR seems to be the default optimization
metric used for training in RankLib, a widely used learning to rank library.
Relates to #29653
4ae4ac08d5