Speaker
Abstract
In this session, we delve into the dynamic world of social media advertising. Facebook, Snap, Pinterest, Twitter, and many others generate the majority of their revenue from targeted ads. We will unpack how Pinterest harnesses the power of Deep Learning Models and big data to tailor relevant advertisements to the pinners. Enjoy a comprehensive walkthrough of the entire advertising funnel and the sophisticated Ads Serving Architecture, before diving deeper into the construction of Deep Learning Models for highly responsive and personalized ads. We will further discuss the challenges faced in industrial-scale models creation and how to meet low-latency requests. Join us as we unravel the complex algorithmic models that fuel social media advertising.
Interview
My current focus is around privacy-safe recommendation systems and ensuring we help bring pinners the inspiration to action by providing the most relevant content.
The field of Ads Recommendation is intricate, and I aim to elucidate its complexities while facilitating a better understanding of how advertising systems operate in general.
The target audience is developers and practitioners, who have a basic understanding of practical machine learning and system building.
To gain insight into the amalgamation of Machine Learning that enables the delivery of personalized and pertinent content, powering the feed of numerous popular applications.
Topics
QCon San Francisco 2023 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
Architectures You've Always Wondered About Hosted by Wes Reisz Technical Principal @Thoughtworks, 16-Time QCon Chair, & Creator of The InfoQ PodcastFrom the same track
Monday 2 October
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