ColdGAN: Resolving Cold Start User Recommendation by using Generative
  Adversarial Networks
  Mitigating the new user cold-start problem has been critical in the
recommendation system for online service providers to influence user experience
in decision making which can ultimately affect the intention of users to use a
particular service. Previous studies leveraged various side information from
users and items; however, it may be impractical due to privacy concerns. In
this paper, we present ColdGAN, an end-to-end GAN based model with no use of
side information to resolve this problem. The main idea of the proposed model
is to train a network that learns the rating distributions of experienced users
given their cold-start distributions. We further design a time-based function
to restore the preferences of users to cold-start states. With extensive
experiments on two real-world datasets, the results show that our proposed
method achieves significantly improved performance compared with the
state-of-the-art recommenders.
