activity
20102021
most citedIRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

568 citations · 2.8k across the 52 of their papers we have counts for

collaborators
Showing 2016Show all

11 papers · 1 filter

cs.LG2016★ 52 cited

Product-based Neural Networks for User Response Prediction

Yanru Qu, Han Cai, Kan Ren +4

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and onl…

cs.GT2016★ 15 cited

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

Jun Wang, Weinan Zhang, Shuai Yuan

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitat…

cs.LG2016★ 294 cited

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

Lantao Yu, Weinan Zhang, Jun Wang +1

As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerabl…

cs.CL2016★ 49 cited

Learning text representation using recurrent convolutional neural network with highway layers

Ying Wen, Weinan Zhang, Rui Luo +1

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model comb…

cs.GT2016★ 57 cited

Feedback Control of Real-Time Display Advertising

Weinan Zhang, Yifei Rong, Jun Wang +2

Real-Time Bidding (RTB) is revolutionising display advertising by facilitating per-impression auctions to buy ad impressions as they are being generated. Being able to use impressi…

cs.GT2016★ 3 cited

Optimal Real-Time Bidding Frameworks Discussion

Weinan Zhang, Kan Ren, Jun Wang

This note is a complementary material for the solution of optimal real-time bidding function in paper "Optimal Real-Time Bidding for Display Advertising, KDD 2014", where the estim…