Light-FMP prunes features and model parameters in deep recommender systems by pretraining a hard-concrete masking layer on data subsets, then retraining the reduced model to improve both efficiency and accuracy over prior methods.
Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1):267–288,
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Light-FMP: Lightweight Feature and Model Pruning for Enhanced Deep Recommender Systems
Light-FMP prunes features and model parameters in deep recommender systems by pretraining a hard-concrete masking layer on data subsets, then retraining the reduced model to improve both efficiency and accuracy over prior methods.