  Click-Through Rate (CTR) prediction is critical for industrial recommender
systems, where most deep CTR models follow an Embedding \& Feature Interaction
paradigm. However, the majority of methods focus on designing network
architectures to better capture feature interactions while the feature
embedding, especially for numerical features, has been overlooked. Existing
approaches for numerical features are difficult to capture informative
knowledge because of the low capacity or hard discretization based on the
offline expertise feature engineering. In this paper, we propose a novel
embedding learning framework for numerical features in CTR prediction (AutoDis)
with high model capacity, end-to-end training and unique representation
properties preserved. AutoDis consists of three core components:
meta-embeddings, automatic discretization and aggregation. Specifically, we
propose meta-embeddings for each numerical field to learn global knowledge from
the perspective of field with a manageable number of parameters. Then the
differentiable automatic discretization performs soft discretization and
captures the correlations between the numerical features and meta-embeddings.
Finally, distinctive and informative embeddings are learned via an aggregation
function. Comprehensive experiments on two public and one industrial datasets
are conducted to validate the effectiveness of AutoDis. Moreover, AutoDis has
been deployed onto a mainstream advertising platform, where online A/B test
demonstrates the improvement over the base model by 2.1% and 2.7% in terms of
CTR and eCPM, respectively. In addition, the code of our framework is publicly
available in
MindSpore(https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/recommend/autodis).
