Generative Adversarial Network Recommendation System with Multi-dimensional Gradient Feedback Mechanism

Abstract

In the Internet era, recommender systems become more and more significant in the daily life. The combination of generative adversarial networks (GAN) and recommended algorithm provides new opportunities for the development of this field.  In previous recommendation systems based on GAN, the gradient feedback provided by the discriminator is binary, which does not comprehensively assist the generator. This inadequacy leads to issues such as unstable generator performance and slow model iteration speed, thereby reducing the overall effectiveness of recommendations. Multi-dimensional gradient feedback generative adversarial networks (MGFGAN) is proposed to address above problems. According to the type of generated multidimensional user rating vector, the model incorporates AutoEncoder in the discriminator to provide more diversified feedback for the generator, aiming to make the generated data more closely match the user’s preferences of the model. However, it brings the problem of increasing computational complexity to the model. Therefore, MGFGAN introduces a negative sampling module in the generator, which makes the generator take into account both items of interest and disinterest to the user, thus accelerating the generator to quickly generate data consistent with the real user distribution and improving the efficiency of the model. Finally, the MGFGAN is carried out experimental simulation on the public datasets FilmTrust and Ciaos. Experimental results show that the recommendation performance of MGFGAN outperforms other recommendation models based on GAN and achieves improvements in efficiency and stability.

Keywords

• recommendation system; multi-dimensional gradient feedback; generative adversarial networks (gan); collaborative filtering