An intelligent recommendation approach for online advertising based on hybrid deep neural network and parallel computing

Artificial neural network is an information process system that simulates the structure and intelligent behavior of the brain based on the understanding of its organizational structure and operating mechanism. In order to deal with the recommendation task for online advertising, this paper proposes a special intelligent recommendation approach based on hybrid deep neural network. This approach integrating embedding mapping network, factorization machine, stacked denoising autoencoder and regression model, can effectively model complex categorical data, learn higher-order abstract features like the brain, and then classify them to achieve the purpose of precise recommendation, and can be well parallelized. Experimental results on the real dataset show that compared with the baseline models, the proposed approach has better performance in the face of scenarios containing a large number of complex categorical data.