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With the rapid evolution of the Internet and smart mobile devices, personalized advertising is becoming increasingly acceptive on we-media platforms. Traditional advertising push cannot meet users’ demand for personalized advertising, leading to users’ resistance to advertising. Aiming to realize personalized advertising recommendation, an advertising recommendation algorithm based on deep learning fusion model is proposed. The bipartite graph model is applied to network representation learning method to decompose user and advertising content into two networks. The embedded representations of two types of nodes are obtained by training GraphSAGE model on their respective networks. The relation matrix of two kinds of nodes is obtained by using the crossproduct operation. Finally, feature information is extracted by convolutional neural network to achieve personalized advertising recommendation. Experimental results verify the effectiveness of the proposed algorithm, which also achieves good results in accuracy and convergence speed.

An Advertising Recommendation Algorithm Based on Deep Learning Fusion Model