Abstract: Learning sophisticated feature interactions is crucial for Click-Through Rate
(CTR) prediction in recommender systems. Various deep CTR models follow an
Embedding & Feature Interaction paradigm. The majority focus on designing
network architectures in Feature Interaction module to better model feature
interactions while the Embedding module, serving as a bottleneck between data
and Feature Interaction module, has been overlooked. The common methods for
numerical feature embedding are Normalization and Discretization. The former
shares a single embedding for intra-field features and the latter transforms
the features into categorical form through various discretization approaches.
However, the first approach surfers from low capacity and the second one limits
performance as well because the discretization rule cannot be optimized with
the ultimate goal of CTR model. To fill the gap of representing numerical
features, in this paper, we propose AutoDis, a framework that discretizes
features in numerical fields automatically and is optimized with CTR models in
an end-to-end manner. Specifically, we introduce a set of meta-embeddings for
each numerical field to model the relationship among the intra-field features
and propose an automatic differentiable discretization and aggregation approach
to capture the correlations between the numerical features and meta-embeddings.
Comprehensive experiments on two public and one industrial datasets are
conducted to validate the effectiveness of AutoDis over the SOTA methods.