Product advertising recommendation in e-commerce based on deep learning and distributed expression

With the advent of Internet big data era, recommendation system has become a hot research topic of information selection. This paper studies the application of deep learning and distributed expression technology in e-commerce product advertising recommendation. In this paper, firstly, from the semantic level of advertising, we build a similarity network based on the theme distribution of advertising, and then build a deep learning model framework for advertising click through rate prediction. Finally, we propose an improved recommendation algorithm based on recurrent neural network and distributed expression. Aiming at the particularity of the recommendation algorithm, this paper improves the traditional recurrent neural network, and introduces a time window to control the hidden layer data transfer of the recurrent neural network. The experimental results show that the improved recurrent neural network model based on time window is superior to the traditional recurrent neural network model in the accuracy of recommendation system. The complexity of calculation is reduced and the accuracy of recommendation system is improved.