In this episode of the Mobile Dev Memo Podcast, host Eric Sufert interviews Matt Steiner from Meta about how ad ranking works on Meta's platform. Matt explains the process of how a bid becomes an ad, detailing the multi-pass ranking operation involving lightweight and heavyweight ranking models, and how the expected value of an ad is determined through auction theory. They discuss the purpose of ad ranking, which is to select ads that drive desired outcomes for advertisers while satisfying users, and how this differs from content ranking. The conversation covers Meta's technological advancements like Lattice, Andromeda, and Gem, focusing on transfer learning, generative AI for creatives, personalized ad retrieval, and sequence learning to improve ad performance and user experience. Matt emphasizes the importance of testing for advertisers to optimize their return on ad spend and the role of AI in automating and enhancing ad performance.
