  Feature quality has an impactful effect on recommendation performance.
Thereby, feature selection is a critical process in developing deep
learning-based recommender systems. Most existing deep recommender systems,
however, focus on designing sophisticated neural networks, while neglecting the
feature selection process. Typically, they just feed all possible features into
their proposed deep architectures, or select important features manually by
human experts. The former leads to non-trivial embedding parameters and extra
inference time, while the latter requires plenty of expert knowledge and human
labor effort. In this work, we propose an AutoML framework that can adaptively
select the essential feature fields in an automatic manner. Specifically, we
first design a differentiable controller network, which is capable of
automatically adjusting the probability of selecting a particular feature
field; then, only selected feature fields are utilized to retrain the deep
recommendation model. Extensive experiments on three benchmark datasets
demonstrate the effectiveness of our framework. We conduct further experiments
to investigate its properties, including the transferability, key components,
and parameter sensitivity.
