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Candidate Set Sampling for Evaluating Top-N Recommendation
The strategy for selecting candidate sets -- the set of items that the recommendation system is expected to rank for each user -- is an important decision in carrying out an offline top-$N$ recommender system evaluation.
Inference at Scale Significance Testing for Large Search and Recommendation Experiments
However, these studies are focused on TREC-style experiments, which typically have fewer than 100 topics.
Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse
In this paper, we argue that the use of statistical inference is a key component of the evaluation process that has not been given sufficient attention.
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