ERM-based PU classifiers designed for case-control sampling deteriorate under single-sample scenarios, requiring a change in the empirical risk definition; a single-sample analogue of the non-negative risk classifier is introduced and shown to differ notably when many positives are labeled.
Recommendations as Treatments: Debiasing Learning and Evaluation
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abstract
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself. In this paper, we provide a principled approach to handling selection biases, adapting models and estimation techniques from causal inference. The approach leads to unbiased performance estimators despite biased data, and to a matrix factorization method that provides substantially improved prediction performance on real-world data. We theoretically and empirically characterize the robustness of the approach, finding that it is highly practical and scalable.
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cs.LG 1years
2023 1verdicts
UNVERDICTED 1representative citing papers
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Single-sample versus case-control sampling scheme for Positive Unlabeled data: the story of two scenarios
ERM-based PU classifiers designed for case-control sampling deteriorate under single-sample scenarios, requiring a change in the empirical risk definition; a single-sample analogue of the non-negative risk classifier is introduced and shown to differ notably when many positives are labeled.