Adversarial recommendation model based on user-specific space
Yu Xiaoming1,，Zhou Gan 1，Liu Zhichun 2
(1National Computer System Engineering Research Institute of China, Beijing 100083, China; 2 63850 Troop, Baicheng 137000, China)
Abstract： Autoencoder and generative adversarial networks, as powerful models, have been applied to the field of recommendation systems to supplement the interactive information between users and items However, under this mode of training, a large amount of auxiliary information is wasted, such as userspecific information This paper combines autoencoder, generative adversarial networks, and auxiliary information to propose an adversarial recommendation model based on userspecific space (USSGAN). In order to establish the connection between auxiliary information and interactive information, the hidden space of the autoencoder is replaced by the userspecific space The pointtopoint mapping of interactive information and userspecific information limits the representation of the model Therefore, the adversarial training is added to the userspecific space to improve the performance of the modelExtensive experiments on two public movie datasets demonstrate the effectiveness and superiority of the proposed model
Key words : generative adversarial networks; userspecific space; autoencoder; recommendation systems
