  Recently, Rendle has warned that the use of sampling-based top-$k$ metrics
might not suffice. This throws a number of recent studies on deep
learning-based recommendation algorithms, and classic non-deep-learning
algorithms using such a metric, into jeopardy. In this work, we thoroughly
investigate the relationship between the sampling and global top-$K$ Hit-Ratio
(HR, or Recall), originally proposed by Koren[2] and extensively used by
others. By formulating the problem of aligning sampling top-$k$ ($SHR@k$) and
global top-$K$ ($HR@K$) Hit-Ratios through a mapping function $f$, so that
$SHR@k\approx HR@f(k)$, we demonstrate both theoretically and experimentally
that the sampling top-$k$ Hit-Ratio provides an accurate approximation of its
global (exact) counterpart, and can consistently predict the correct winners
(the same as indicate by their corresponding global Hit-Ratios).
