  Feature selection has been an essential step in developing industry-scale
deep Click-Through Rate (CTR) prediction systems. The goal of neural feature
selection (NFS) is to choose a relatively small subset of features with the
best explanatory power as a means to remove redundant features and reduce
computational cost. Inspired by gradient-based neural architecture search (NAS)
and network pruning methods, people have tackled the NFS problem with Gating
approach that inserts a set of differentiable binary gates to drop less
informative features. The binary gates are optimized along with the network
parameters in an efficient end-to-end manner. In this paper, we analyze the
gradient-based solution from an exploration-exploitation perspective and use
empirical results to show that Gating approach might suffer from insufficient
exploration. To improve the exploration capacity of gradient-based solutions,
we propose a simple but effective ensemble learning approach, named Ensemble
Gating. We choose two public datasets, namely Avazu and Criteo, to evaluate
this approach. Our experiments show that, without adding any computational
overhead or introducing any hyper-parameter (except the size of the ensemble),
our method is able to consistently improve Gating approach and find a better
subset of features on the two datasets with three different underlying deep CTR
prediction models.
