Abstract
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.
Highlights
With the rapid development of the Internet and intelligent mobile devices, human society has entered the era of information explosion since mobile Internet advertising is booming, and higher requirements for advertising recommendation are put forward [1]
In order to realize personalized advertising recommendation, this paper proposes an advertising recommendation algorithm based on deep learning fusion model
The GraphSAGE model is constructed for the problem of homogeneous graph, and the embedded representation of nodes is generated through node attribute information and network structure information
Summary
With the rapid development of the Internet and intelligent mobile devices, human society has entered the era of information explosion since mobile Internet advertising is booming, and higher requirements for advertising recommendation are put forward [1]. The traditional advertising recommendation method often pushes some uninteresting or even irrelevant advertising content to users, which will reduce the normal access of users, and some of which will even steal users’ privacy. This kind of “carpet bombing” promotion method causes great dislike of users [2]. In order to realize personalized advertising recommendation, this paper proposes an advertising recommendation algorithm based on deep learning fusion model. (1) In order to divide the user and the advertising information into two disjoint subsets, the bipartite graph model is applied to the network representation learning method to divide the user and the advertising content into two networks.
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