  Generative adversarial nets (GANs) have been widely studied during the recent
development of deep learning and unsupervised learning. With an adversarial
training mechanism, GAN manages to train a generative model to fit the
underlying unknown real data distribution under the guidance of the
discriminative model estimating whether a data instance is real or generated.
Such a framework is originally proposed for fitting continuous data
distribution such as images, thus it is not straightforward to be directly
applied to information retrieval scenarios where the data is mostly discrete,
such as IDs, text and graphs. In this tutorial, we focus on discussing the GAN
techniques and the variants on discrete data fitting in various information
retrieval scenarios. (i) We introduce the fundamentals of GAN framework and its
theoretic properties; (ii) we carefully study the promising solutions to extend
GAN onto discrete data generation; (iii) we introduce IRGAN, the fundamental
GAN framework of fitting single ID data distribution and the direct application
on information retrieval; (iv) we further discuss the task of sequential
discrete data generation tasks, e.g., text generation, and the corresponding
GAN solutions; (v) we present the most recent work on graph/network data
fitting with node embedding techniques by GANs. Meanwhile, we also introduce
the relevant open-source platforms such as IRGAN and Texygen to help audience
conduct research experiments on GANs in information retrieval. Finally, we
conclude this tutorial with a comprehensive summarization and a prospect of
further research directions for GANs in information retrieval.
