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KDD 2020 | On Sampling Top-K Recommendation Evaluation

Accepted Papers

On Sampling Top-K Recommendation Evaluation

Dong Li: Kent State University; Ruoming Jin: Kent State University; Jing Gao: iLambda; Zhi Liu: iLambda


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).

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