Web: http://arxiv.org/abs/1910.01444

June 16, 2022, 1:12 a.m. | Yuta Saito

cs.LG updates on arXiv.org arxiv.org

In most real-world recommender systems, the observed rating data are subject
to selection bias, and the data are thus missing-not-at-random. Developing a
method to facilitate the learning of a recommender with biased feedback is one
of the most challenging problems, as it is widely known that naive approaches
under selection bias often lead to suboptimal results. A well-established
solution for the problem is using propensity scoring techniques. The propensity
score is the probability of each data being observed, and unbiased …

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