Enhanced network for CTR prediction based on field-matrixed factorization machines
Effective feature interaction plays a vital role in the accuracy of click-through-rate(CTR)estimation in industri-al recommendation systems.Previous CTR prediction models with a parallel structure learn low-order and high-order interac-tions of features by connecting independent shallow models and deep models in parallel.However,these models have prob-lems such as low accuracy of shallow models,failure to consider the multi-semantic problem of feature interaction,exces-sive parameters,and over-generalization of deep models.Based on the above problems,this paper proposes an enhanced network for CTR prediction based on field-matrixed factorization machines.It introduces domain matrix to optimize the inter-action in shallow models,improves the efficiency of computation,and adds a bridge module between the DNN layers of deep models to enhance the memory ability of original features after each high-order interaction.The results of shallow and deep models are added and normalized to obtain the predicted value.The model has undergone extensive experiments on Criteo,KKBox,Frappe,and MovieLens datasets,demonstrating excellent predictive capabilities.
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