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.

中文翻译：

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基于深度学习和分布式表达的电子商务产品广告推荐

随着互联网大数据时代的到来，推荐系统已成为信息选择的研究热点。本文研究了深度学习和分布式表达技术在电子商务产品广告推荐中的应用。本文首先从广告的语义层次出发，建立基于广告主题分布的相似度网络，然后建立广告点击率预测的深度学习模型框架。最后，提出了一种基于递归神经网络和分布式表达的改进推荐算法。针对推荐算法的特殊性，本文对传统的递归神经网络进行了改进，引入了时间窗口来控制递归神经网络的隐层数据传输。实验结果表明，改进的基于时间窗的递归神经网络模型在推荐系统的准确性上优于传统的递归神经网络模型。降低了计算复杂度，提高了推荐系统的准确性。