We introduce a new way to set personalized reservation prices in impression-based ad auctions. By combining game theory and deep neural networks, we can infer advertisers’ hidden willingness to pay directly from their bidding data, and we use this information to optimize auction prices. Accurately estimating and acting on advertisers’ true valuation is crucial, as even small improvements in price setting can translate into substantial revenue gains and more efficient market outcomes. Tested on 100,000 real auctions, our approach allows for a significant increase of the expected auction revenue by an average of +4% across ten markets.

Spotify_Research's publication is a hub for academic research and industry insights in the field of music streaming technology and user behavior analysis. Through research papers, data analysis, and experimental studies, Spotify_Research offers  insights into music recommendation algorithms, user engagement metrics, and content discovery mechanisms. Readers can learn about the latest advancements in music streaming technology, personalized playlist generation, and user-centric design principles to enhance the music listening experience. Additionally, Spotify_Research provides data-driven insights, user studies, and market analysis to help music enthusiasts and industry professionals understand the evolving landscape of digital music consumption and anticipate future trends.

Spotify Research

Spotify Research developed a method combining game theory and deep neural networks to optimize personalized reservation prices in ad auctions. The approach uses Mixture Density Networks with embedded Nash equilibrium constraints to infer advertisers' hidden willingness to pay from bidding data. Tested on 100,000 real auctions across ten markets, the system achieved an average 4% revenue increase (up to 11.8% in some markets) by setting dynamic prices based on user features and advertiser characteristics. The method bridges economic theory with machine learning to create more efficient ad marketplaces.

Learning Personalised Prices in Ad Auctions with Game Theory and Deep Learning

Results: Real-world data and +4% revenue lift