A Sparse Deep Factorization Machine for Efficient CTR prediction
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Click-through rate (CTR) prediction is a crucial task in online display advertising and the key part is to learn important feature interactions. The mainstream models are embedding-based neural networks that provide end-to-end training by incorporating hybrid components to model both low-order and high-order feature interactions. These models, however, slow down the prediction inference by at least hundreds of times due to the deep neural network (DNN) component. Considering the challenge of deploying embedding-based neural networks for online advertising, we propose to prune the redundant parameters for the first time to accelerate the inference and reduce the run-time memory usage. Most notably, we can accelerate the inference by 46X on Criteo dataset and 27X on Avazu dataset without loss on the prediction accuracy. In addition, the deep model acceleration makes an efficient model ensemble possible with low latency and significant gains on the performance.
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