34 research outputs found
Conversion-Based Dynamic-Creative-Optimization in Native Advertising
Yahoo Gemini native advertising marketplace serves billions of impressions
daily, to hundreds millions of unique users, and reaches a yearly revenue of
many hundreds of millions USDs. Powering Gemini native models for predicting
advertise (ad) event probabilities, such as conversions and clicks, is OFFSET -
a feature enhanced collaborative-filtering (CF) based event prediction
algorithm. The predicted probabilities are then used in Gemini native auctions
to determine which ads to present for every serving event (impression). Dynamic
creative optimization (DCO) is a recent Gemini native product that was launched
two years ago and is increasingly gaining more attention from advertisers. The
DCO product enables advertisers to issue several assets per each native ad
attribute, creating multiple combinations for each DCO ad. Since different
combinations may appeal to different crowds, it may be beneficial to present
certain combinations more frequently than others to maximize revenue while
keeping advertisers and users satisfied. The initial DCO offer was to optimize
click-through rates (CTR), however as the marketplace shifts more towards
conversion based campaigns, advertisers also ask for a {conversion based
solution. To accommodate this request, we present a post-auction solution,
where DCO ads combinations are favored according to their predicted conversion
rate (CVR). The predictions are provided by an auxiliary OFFSET based
combination CVR prediction model, and used to generate the combination
distributions for DCO ad rendering during serving time. An online evaluation of
this explore-exploit solution, via online bucket A/B testing, serving Gemini
native DCO traffic, showed a 53.5% CVR lift, when compared to a control bucket
serving all combinations uniformly at random.Comment: Accepted to IEEE Big Data 2022 conferenc
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems
Technologies and Applications for Big Data Value
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems