Advertising Science

We combine our immense trove of data with tools from large-scale machine learning, statistical modeling, feedback control, and more to develop the high-performing and robust algorithms that drive impression allocation and ad serving. including machine learning, statistics, operations research, economics, mathematics, control theory, etc. Some representative areas of work are: click and conversion prediction, viewability and video completion prediction, incremental reach optimization, campaign budget spend pacing, bidding strategies, supply path optimization, app marketing optimization, and optimization and performance measurement on internet traffic with privacy-driven restricted user traceability.

Publications

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January 1, 2019
Journal of Marketing

Serial Position Effects on Native Advertising Effectiveness: Differential Results Across Publisher and Advertiser Metrics

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January 1, 2019
IEEE International Symposium on Technology and Society (IEEE ISTAS 2019)

The Future of Online Advertising: Thoughts on Emerging Issues in Privacy, Information Bubbles and Disinformation

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January 1, 2019
IEEE Transactions on Knowledge and Data Engineering

Position-Aware Deep Character-Level CTR Prediction for Sponsored Search

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January 1, 2019
KDD

Carousel Ads Optimization in Yahoo Gemini Native

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January 1, 2019
KDD

Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising

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January 1, 2019
RecSys

Guiding Creative Design in Online Advertising