  Click-Through Rate (CTR) prediction has been an indispensable component for
many industrial applications, such as recommendation systems and online
advertising. CTR prediction systems are usually based on multi-field
categorical features, i.e., every feature is categorical and belongs to one and
only one field. Modeling feature conjunctions is crucial for CTR prediction
accuracy. However, it requires a massive number of parameters to explicitly
model all feature conjunctions, which is not scalable for real-world production
systems. In this paper, we describe a novel Field-Leveraged Embedding Network
(FLEN) which has been deployed in the commercial recommender system in Meitu
and serves the main traffic. FLEN devises a field-wise bi-interaction pooling
technique. By suitably exploiting field information, the field-wise
bi-interaction pooling captures both inter-field and intra-field feature
conjunctions with a small number of model parameters and an acceptable time
complexity for industrial applications. We show that a variety of
state-of-the-art CTR models can be expressed under this technique. Furthermore,
we develop Dicefactor: a dropout technique to prevent independent latent
features from co-adapting. Extensive experiments, including offline evaluations
and online A/B testing on real production systems, demonstrate the
effectiveness and efficiency of FLEN against the state-of-the-arts. Notably,
FLEN has obtained 5.19% improvement on CTR with 1/6 of memory usage and
computation time, compared to last version (i.e. NFM).
