Recently, Rendle has warned that the use of sampling-based topmetrics 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-Hit-Ratio (HR, or Recall), originally proposed by Koren [2] and extensively used by others. By formulating the problem of aligning sampling top-( @ ) and global top-( @ ) Hit-Ratios through a mapping function , so that @ ≈ @ ( ), we demonstrate both theoretically and experimentally that the sampling top-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).
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