Title
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Abstract
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair. We propose a game theoretical minimax game to iteratively optimise both models. On one hand, the discriminative model, aiming to mine signals from labelled and unlabelled data, provides guidance to train the generative model towards fitting the underlying relevance distribution over documents given the query. On the other hand, the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination objective. With the competition between these two models, we show that the unified framework takes advantage of both schools of thinking: (i) the generative model learns to fit the relevance distribution over documents via the signals from the discriminative model, and (ii) the discriminative model is able to exploit the unlabelled data selected by the generative model to achieve a better estimation for document ranking. Our experimental results have demonstrated significant performance gains as much as 23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of applications including web search, item recommendation, and question answering.
Year
DOI
Venue
2017
10.1145/3077136.3080786
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
DocType
Volume
Citations 
Conference
abs/1705.10513
114
PageRank 
References 
Authors
2.48
43
8
Search Limit
100114
Name
Order
Citations
PageRank
Jun Wang12514138.37
Lantao Yu236416.32
Weinan Zhang3122897.24
Yu Gong41328.35
Yinghui Xu517220.23
Benyou Wang616815.83
Peng Zhang71315.59
Dell Zhang8106157.54