A generative adversarial network for generating realistic users using embedding from recommendation systems

Abstract

User data has been used by many companies to understand user behaviors and find new business strategies. However, common techniques could not be used when it comes to new products that have not yet been released due to the fact that there are no prior data available. In this work, we propose a framework for generating realistic user data on new products which can then be analyzed for insights. Our model uses Conditional Generative Adversarial Network (CGAN) with the Straight-Through Gumbel estimator which can also handle discrete-valued outputs. The CGAN is conditioned on product features learned using a recommendation system which can better capture the relationship between products. Experiments using a dataset consisting of view logs from a real estate listing website shows that our model outperforms other baselines on four performance metrics and can effectively predict the finer characteristics of new products.