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≈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).
@article{arxiv.2106.10621,
title = {On Sampling Top-K Recommendation Evaluation},
author = {Dong Li and Ruoming Jin and Jing Gao and Zhi Liu},
journal= {arXiv preprint arXiv:2106.10621},
year = {2021}
}