SVV prunes recommender training interactions by their estimated Shapley value contribution to autoencoder loss reduction, reporting modest accuracy gains on four datasets but resting on a faulty value-function derivation.
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Shapley Value-driven Data Pruning for Recommender Systems
SVV prunes recommender training interactions by their estimated Shapley value contribution to autoencoder loss reduction, reporting modest accuracy gains on four datasets but resting on a faulty value-function derivation.