Learning, Prediction and Optimisation in RTB Display Advertising

A CIKM 2016 Tutorial by Weinan Zhang and Jian Xu

This website is for the CIKM 2016 tutorial application: Learning, Prediction and Optimisation in RTB Display Advertising.

The tutorial would be provided by Weinan Zhang (Shanghai Jiao Tong University) and Jian Xu (TouchPal Inc.) on Octobor 24, 2016 in the CIKM conference Indianapolis, United States.

Tutorial Slides

Supporting Documents

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 CIKM researchers a few datasets recently made available so that they can get hands-on quickly and enable the said research.



Weinan Zhang recently received his Ph.D. from University College London and is now an assistant professor in Shanghai Jiao Tong University. 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, AAAI, 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.

Jian Xu is currently Principal Data Scientist at TouchPal Inc., Mountain View, CA, where he is in charge of the overall monetization solutions. Before joining TouchPal, he served as Senior Data Scientist and Senior Research Engineer at Yahoo Inc., responsible for various advertising related technologies such as response prediction, budget pacing, and bid optimization. His research interests center around Data Mining, Machine Learning, and Computational Advertising. His recent research includes developing high performance advertising systems and monetization from massive data. He has published or filed more than 10 US patents and published research papers in top academic conferences and journals such as SIGKDD, AAAI, ICDCS, and SIGKDD Explorations, which received more than 500 citations. He also served as reviewer for well-recognized journals including TKDE, TIST, WWW, KAIS, Big Data Research and is on the Editorial Advisory Board of Information Systems (Elsevier).

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