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.
Differentiable neural input search for recom- mender systems.arXiv preprint arXiv:2006.04466
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.IR 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.