Real-Time Bidding based Display Advertising: Mechanisms and Algorithms

An ECIR 2016 Tutorial by Jun Wang, Shuai Yuan, and Weinan Zhang

This website is for the ECIR 2016 Tutorial: Real-Time Bidding based Display Advertising: Mechanisms and Algorithms.

The tutorial has been given by Dr. Jun Wang, Dr. Shuai Yuan, and Weinan Zhang in March, 2016 during the ECIR conference in Padua, Italy.

Download the final slides, or read it on Slideshare.

Tutorial history

Brief introduction

In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. The ability of making impression level bid decision and targeting to an individual user in real-time has fundamentally changed the landscape of the digital media. The further demand for automation, integration and optimisation in RTB brings new research opportunities in the IR fields, including information matching with economic constraints, CTR prediction, user behaviour targeting and profiling, personalised advertising, and attribution and evaluation methodologies. In this tutorial, teamed up with presenters from both the industry and academia, we aim to bring the insightful knowledge from the real-world systems, and to provide an overview of the fundamental mechanism and algorithms with the focus on the IR context. We will also introduce to IR researchers a few datasets recently made available so that they can get hands-on quickly and enable the said research.



Dr. Jun Wang is a Reader (Associate Professor) in University College London. He has published over 70 research papers in leading journals and conference proceedings including ACM Trans. on Information Systems, IEEE Trans. on Multimedia, ACM Multimedia System Journal, WWW, CIKM, ACM SIGIR, SIGMM. He received the Best Doctoral Consortium award in ACM SIGIR06 for his work on collaborative filtering, the Best Paper Prize in ECIR09 for his work on applying Modern Portfolio Theory of Finance (Mean-variance Analysis) to document ranking in Information Retrieval, and the Best Paper Prize in ECIR12 for top-k retrieval modelling. He has extensive experiences in giving tutorials on top conferences: his recent tutorials about risk management and portfolio theory of information retrieval were given in CIKM2011 and ECIR2011.

Dr. Shuai Yuan recently received his Ph.D. from University College London and is now a Data Scientist at MediaGamma. He has been working on mathematical models of online advertising with a number of companies such as AppNexus, Advance International Media, Bright,, and Miaozhen. He has the background of information retrieval, data mining, machine learning, and economic theories; his research interests on computational advertising have focused on supply side optimisation in RTB, bidding algorithms, and statistical arbitrage. Shuai Yuan has published several papers in top-tier venues including CIKM, SIGKDD, and ADKDD. Among them, he published the first empirical study on RTB auctions. He and his colleagues won the third season of iPinyou Global Bidding Algorithm Competition in 2013, and the Best Paper Award of ADKDD 2014. He also contributes to an open advertising dataset project.

Weinan Zhang is completing his Ph.D. in University College London. His research interests include machine learning, dynamic optimisation and their applications in RTB based display advertising and recommender systems. Particularly, He focuses on the research of optimal DSP bidding strategies for RTB display advertising. He is also interested in deep learning models and has developed several domain-specified DNNs for predicting users' online commercial behaviours. Weinan Zhang has published more than 20 papers in top international conferences including SIGKDD, CIKM, SIGIR, RecSys and WI. He also has made publications in well-recognised journals including ACM TIST, IPM, and JMLR. He and Dr. Shuai Yuan won the final session of iPinyou Global Bidding Algorithm Competition in 2013.

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