Layer-by-Layer Residual Interactive Network Approach for Advertisement Click-Through Rate Prediction
Online advertising fees are charged based on the number of times that users click on ads,and therefore how to accurately predict Click-Through Rate(CTR)is a very concerned issue for advertising companies.Current state-of-the-art methods focus on constructing various high-order feature interaction models to predict CTR;however,high-order feature interactions will lose low-order information,especially the information of original features.To this end,a novel layer-by-layer residual interaction network framework is proposed in this paper,which leverages the guiding role of the original features at each interaction,and is named as the Layer-by-layer Residual Interaction Network(LRIN).LRIN emphasizes that higher-order feature interactions should be based on the interactions of original features layer by layer.The interaction of n-order features is obtained by the element-wise product between the original features and the n-1-order features.Moreover,a multi-scale approach is introduced to design attention network.Affected by layer-by-layer interaction,the attention network is also designed into multiple layers,which is called layer-by-layer attention networks.In order to combine the two,this paper proposes to take the outputs of the layer-by-layer residual interaction network as the weights of the layer-by-layer attention network,and thus forms a novel dual-network training model.The experimental results on multiple benchmark datasets indicate that the performance of LRIN is on average 1.24％better than current advanced methods on the Criteo dataset,2.16％better on the Avazu dataset,1.3％better on the MovieLens-1M dataset,and 1.27％better on the Book-crossing dataset.
万方数据
维普
