  Click-through rate (CTR) prediction is a crucial task in online display
advertising. The embedding-based neural networks have been proposed to learn
both explicit feature interactions through a shallow component and deep feature
interactions using a deep neural network (DNN) component. These sophisticated
models, however, slow down the prediction inference by at least hundreds of
times. To address the issue of significantly increased serving delay and high
memory usage for ad serving in production, this paper presents
\emph{DeepLight}: a framework to accelerate the CTR predictions in three
aspects: 1) accelerate the model inference via explicitly searching informative
feature interactions in the shallow component; 2) prune redundant layers and
parameters at intra-layer and inter-layer level in the DNN component; 3)
promote the sparsity of the embedding layer to preserve the most discriminant
signals. By combining the above efforts, the proposed approach accelerates the
model inference by 46X on Criteo dataset and 27X on Avazu dataset without any
loss on the prediction accuracy. This paves the way for successfully deploying
complicated embedding-based neural networks in production for ad serving.
