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Normalization has become one of the most fundamental components in many deepneural networks for machine learning tasks while deep neural network has alsobeen widely used in CTR estimation field. Among most of the proposed deepneural network models, few model utilize normalization approaches. Though someworks such as Deep & Cross Network (DCN) and Neural Factorization Machine (NFM)use Batch Normalization in MLP part of the structure, there isn't work tothoroughly explore the effect of the normalization on the DNN ranking systems.In this paper, we conduct a systematic study on the effect of widely usednormalization schemas by applying the various normalization approaches to bothfeature embedding and MLP part in DNN model. Extensive experiments are conducton three real-world datasets and the experiment results demonstrate that thecorrect normalization significantly enhances model's performance. We alsopropose a new and effective normalization approaches based on LayerNorm namedvariance only LayerNorm(VO-LN) in this work. A normalization enhanced DNN modelnamed NormDNN is also proposed based on the above-mentioned observation. As forthe reason why normalization works for DNN models in CTR estimation, we findthat the variance of normalization plays the main role and give an explanationin this work.

Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction