Trust-Aware Generative Adversarial Network With Recurrent Neural Network For Recommender Systems

Recently recommender systems become more and more significant in the daily life such as event recommendation, content recommendation and commodity recommendation, and so forth. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration to improve the recommendation accuracy. In this paper, we propose a Trust-Aware GAN with recurrent neural network (RNN) for RECommender systems named TagRec, which makes use of the user trust information for top-N recommendation. In the framework, the discriminative model is a multilayer perceptron to distinguish whether a sample is from the real data or fake data generated by the generative model. The discriminator helps to guide the training of the generative model to make it fit the data distribution of the user trust information. The generative model is a RNN with long short-term memory cells, aiming to confuse the discriminative model by generating samples as similar as possible to the real data. Through the adversarial training between the discriminative and generative models, the user trust information can be fully used to improve the recommendation performance. We conduct extensive experiments on real-word data sets to validate the effectiveness of the TagRec by comparing it with the benchmarks.