  Input features play a crucial role in DNN-based recommender systems with
thousands of categorical and continuous fields from users, items, contexts, and
interactions. Noisy features and inappropriate embedding dimension assignments
can deteriorate the performance of recommender systems and introduce
unnecessary complexity in model training and online serving. Optimizing the
input configuration of DNN models, including feature selection and embedding
dimension assignment, has become one of the essential topics in feature
engineering. However, in existing industrial practices, feature selection and
dimension search are optimized sequentially, i.e., feature selection is
performed first, followed by dimension search to determine the optimal
dimension size for each selected feature. Such a sequential optimization
mechanism increases training costs and risks generating suboptimal input
configurations. To address this problem, we propose a differentiable neural
input razor (i-Razor) that enables joint optimization of feature selection and
dimension search. Concretely, we introduce an end-to-end differentiable model
to learn the relative importance of different embedding regions of each
feature. Furthermore, a flexible pruning algorithm is proposed to achieve
feature filtering and dimension derivation simultaneously. Extensive
experiments on two large-scale public datasets in the Click-Through-Rate (CTR)
prediction task demonstrate the efficacy and superiority of i-Razor in
balancing model complexity and performance.
