A Hybrid Neural Network Architecture to Predict Online Advertising Click-Through Rate Behaviors in Social Networks

People connected by huge-size socialnetworks are highly dependent on recommendation systems to discover interesting persons, contents, and commodities. Extracting user interests and modeling their evolutions from user historical behavior are vital for algorithms to judge whether users are interested in given persons and items. The existing methods have two shortcomings: firstly, they do not have the high-order ability to process temporal sequences, which leads to their inability to mine the evolution of user interests; secondly, the models take full large-scale information of items as input, which causes a severe problem of overfitting. Thus, we propose a new hybrid neural network, DGRU, which integrates Factorization-Machine Based Neural Network (DeepFM) and Gated Recurrent Unit Neural Network (GRU). The DeepFM component is responsible for performing the autonomic feature combination, and the GRU component is designed to model user interests and evolutions. The GRU component is fed with a series of 1 and 0 representing user click behaviors. It contains information on what users like and dislike. Moreover, the conciseness of the format is helpful to avoid the problem of overfitting. Experiments on three real datasets demonstrated that the proposed model has a better CTR prediction performance and robustness than the state-of-the-art models.

Automated summary

This study introduces a new hybrid neural network model, DGRU, which integrates DeepFM and GRU components, with DeepFM performing autonomic feature combination and GRU modeling user interests and evolutions. The GRU component is fed with a series of 1 and 0 representing user click behaviors, distinguishing between user likes and dislikes. Experiments demonstrate that the proposed model outperforms existing models in CTR prediction performance and robustness.