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Information retrieval systems are usually measured by labeling the relevanceof results corresponding to a sample of user queries. In practical searchengines, such measurement needs to be performed continuously, such as daily orweekly. This creates a trade-off between (a) representativeness of query sampleto current query traffic of the product; (b) labeling cost: if we keep the samequery sample, results would be similar allowing us to reuse their labels; and(c) overfitting caused by continuous usage of same query sample. In this paperwe explicitly formulate this tradeoff, propose two new variants -- Stable andSemi-stable -- to simple and weighted random sampling and show that theyoutperform existing approaches for the continuous usage settings, includingmonitoring/debugging search engine or comparing ranker candidates.

Stable and Semi-stable Sampling Approaches for Continuously Used Samples