  Click-through rate (CTR) prediction is a fundamental technique in
recommendation and advertising systems. Recent studies have proved that
learning a unified model to serve multiple domains is effective to improve the
overall performance. However, it is still challenging to improve generalization
across domains under limited training data, and hard to deploy current
solutions due to their computational complexity. In this paper, we propose a
simple yet effective framework AdaSparse for multi-domain CTR prediction, which
learns adaptively sparse structure for each domain, achieving better
generalization across domains with lower computational cost. In AdaSparse, we
introduce domain-aware neuron-level weighting factors to measure the importance
of neurons, with that for each domain our model can prune redundant neurons to
improve generalization. We further add flexible sparsity regularizations to
control the sparsity ratio of learned structures. Offline and online
experiments show that AdaSparse outperforms previous multi-domain CTR models
significantly.
