Empirical Bayes 1-bit matrix completion

Abstract:The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as recommendation systems. In this study, we develop an empirical Bayes method for 1-bit matrix completion motivated by the Efron--Morris estimator, a matrix generalization of the James--Stein estimator that shrinks singular values toward zero. The proposed method exploits the underlying low-rank structure of binary matrices, drawing parallels with multidimensional item response theory. Simulation studies and real-data applications demonstrate that the proposed method achieves competitive predictive accuracy and favorable predictive calibration.

Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME)

arXiv:2605.09509 [stat.ML]

(or arXiv:2605.09509v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2605.09509 Focus to learn more arXiv-issued DOI via DataCite

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