Prediction of Click-through Rate of Marketing Advertisements Using Deep Learning

Aiming at the defect that click-through rate of marketing advertisements cannot provide accurate prediction results for company in time strategy Internet companies, this paper uses a deep learning algorithm to establish model advertisements. The suggested is called high-order cross-feature network (HCN). Furthermore, also introduces combination feature vectors into graph structure and as nodes graph; therefore, neural (GNN) used obtain high-level representation ability structured data more fully. Through numerical simulations, we observed HCN has capability companies with advertising business information, user content. Moreover, reasonable adjust can better experience. simulation outcomes indicate approach noble adaptability high correctness forecasting We improvement, terms predictions precisions accuracies, be 17.52% higher than (DNN) method 10.45% factorization (FM) approach.