SIGIR 2018 Tutorial 

Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances 

Weinan Zhang, Assistant Professor

APEX Data & Knowledge Management Lab
John Hopcroft Center for Computer Science
Department of Computer Science & Engineering
Shanghai Jiao Tong University

Email: wnzhang [AT]

Tutorial Slides

Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances
Weinan Zhang, Shanghai Jiao Tong University
SIGIR 2018

Tutorial Abstract

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.

Speaker Info

Weinan is now a tenure-track assistant professor in Department of Computer Science, Shanghai Jiao Tong University. His research interests include machine learning and big data mining, particularly, deep learning and (multi-agent) reinforcement learning architectures, mechanisms, training algorithms and their applications in real-world data mining scenarios including computational advertising, recommender systems, text mining, web search and knowledge graphs.

Weinan earned his Ph.D. from University College London in 2016 and B.Eng. from ACM Class of Shanghai Jiao Tong University in 2011. He was an intern at MediaGamma, Microsoft Research, Google and DERI.

Tutorial Key Reference Papers

Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances
Weinan Zhang
SIGIR 2018

Generative Adversarial Networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
NIPS 2018

How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary
Ferenc Huszar
ArXiv 2015

NIPS 2016 Tutorial: Generative Adversarial Networks
Ian J. Goodfellow
NIPS 2016

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, Dell Zhang
SIGIR 2017

Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David McAllester, Satinder Singh, Yishay Mansour
NIPS 2000

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, Noam Shazeer
NIPS 2015

Professor Forcing: A New Algorithm for Training Recurrent Networks
Alex Lamb, Anirudh Goyal, Ying Zhang, Saizheng Zhang, Aaron Courville, Yoshua Bengio
NIPS 2016

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Lantao Yu, Weinan Zhang, Jun Wang, Yong Yu
AAAI 2017

Long Text Generation via Adversarial Training with Leaked Information
Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, Jun Wang
AAAI 2018

FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu
ICML 2017

GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
AAAI 2018

Texygen: A Benchmarking Platform for Text Generation Models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, Yong Yu
SIGIR 2018

CoT: Cooperative Training for Generative Modeling
Sidi Lu, Lantao Yu, Weinan Zhang, Yong Yu
ArXiv 2018

Tutorial Bibtex

     title={Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances},
     author={Zhang, Weinan},


8 Jul. 2018
Our tutorial went smoothly with more than 70 audiences. The post-presentation version of tutorial slides has been updated here.

8 Jul. 2018
The presentation version of tutorial slides has been added here.

5 Jul. 2018
The first version of tutorial information has been added here.

28 Mar 2018
Website launched!