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arXiv · 2106.10621

On Sampling Top-K Recommendation Evaluation

Dong Li·Ruoming Jin·Jing Gao·Zhi Liu
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Abstract

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).

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Dong Li, Ruoming Jin, Jing Gao, Zhi Liu. 2021-06-20. On Sampling Top-K Recommendation Evaluation. https://doi.org/10.1145/3394486.3403262

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