Trust‐aware generative adversarial network with recurrent neural network for recommender systems

Abstract

Recently recommender systems become more and more significant in the daily life. Although the recommender systems based on the generative adversarial network (GAN) are competent, the user trust information is seldom taken into consideration. In this paper, we propose a Trust-Aware Generative adversarial network with recurrent neural network 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 multi-layer perceptron to distinguish whether a sample is from the real data or fake data. 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 recurrent neural network (RNN) with long short-term memory (LSTM) 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 datasets to validate the effectiveness of the TagRec.