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Since Rendle and Krichene argued that commonly used sampling-based evaluationmetrics are "inconsistent" with respect to the global metrics (even inexpectation), there have been a few studies on the sampling-based recommendersystem evaluation. Existing methods try either mapping the sampling-basedmetrics to their global counterparts or more generally, learning the empiricalrank distribution to estimate the top-$K$ metrics. However, despite existingefforts, there is still a lack of rigorous theoretical understanding of theproposed metric estimators, and the basic item sampling also suffers from the"blind spot" issue, i.e., estimation accuracy to recover the top-$K$ metricswhen $K$ is small can still be rather substantial. In this paper, we provide anin-depth investigation into these problems and make two innovativecontributions. First, we propose a new item-sampling estimator that explicitlyoptimizes the error with respect to the ground truth, and theoreticallyhighlight its subtle difference against prior work. Second, we propose a newadaptive sampling method which aims to deal with the "blind spot" problem andalso demonstrate the expectation-maximization (EM) algorithm can be generalizedfor such a setting. Our experimental results confirm our statistical analysisand the superiority of the proposed works. This study helps lay the theoreticalfoundation for adopting item sampling metrics for recommendation evaluation,and provides strong evidence towards making item sampling a powerful andreliable tool for recommendation evaluation.

Towards Reliable Item Sampling for Recommendation Evaluation