Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss
Generalizing Linear Autoencoder Recommenders with Decoupled Expected Quadratic Loss explores Improve recommendation accuracy by expanding the solution space of linear autoencoders with a decoupled quadratic loss, enabling better hyperparameter tuning and efficient computation.. Commercial viability score: 7/10 in Recommendation Systems.
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