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Balancing Revenue and Relevance: Deep Learning Meets Domain Ads
In most ad systems, the logic is simple: highest bid wins. But simple doesn’t mean smart. At GoDaddy, we asked a harder question: Can we optimize both revenue and user satisfaction—without sacrificing either?
So, we built DeepAd: a deep learning ranking model that blends monetization, behavior, and intent to deliver ads that are both profitable and meaningful.
Core Concepts
• Context-aware ranking: Goes beyond “highest bid wins,” factoring in intent, engagement, and diversity.
• Transfer learning from DeepRank: Leveraging billions of domain-search interactions to give our ad model a head start.
• ReTiRe framework: Balancing Relevance, Time, and Revenue for a truly adaptive signal mix.
• Real behavioral supervision: Learning directly from shopper actions — what they clicked, bought, or skipped.
Why this matters:
✅ Shoppers see domains that actually fit their intent.
✅ Registries get fair exposure — niche top-level domain (TLD) can compete on merit, not budget.
✅ GoDaddy achieves sustainable revenue without degrading user trust.
Key takeaway: Building intelligent ad systems isn’t just about monetization — it’s about aligning incentives between users, advertisers, and platforms through smarter modeling.
👉 Dive into the technical deep dive: Balancing Revenue and Relevance: Optimizing TLD Ad Rankings https://www.godaddy.com/resources/news/balancing-revenue-and-relevance-optimizing-tld-ad-rankings
Curious to hear your perspective — share your thoughts below!
godaddy
In most ad systems, the logic is simple: highest bid wins. But simple doesn’t mean smart. At GoDaddy, we asked a harder question: Can we optimize both revenue and user satisfaction—without sacrificing either?
So, we built DeepAd: a deep learning ranking model that blends monetization, behavior, and intent to deliver ads that are both profitable and meaningful.
Core Concepts
• Context-aware ranking: Goes beyond “highest bid wins,” factoring in intent, engagement, and diversity.
• Transfer learning from DeepRank: Leveraging billions of domain-search interactions to give our ad model a head start.
• ReTiRe framework: Balancing Relevance, Time, and Revenue for a truly adaptive signal mix.
• Real behavioral supervision: Learning directly from shopper actions — what they clicked, bought, or skipped.
Why this matters:
✅ Shoppers see domains that actually fit their intent.
✅ Registries get fair exposure — niche top-level domain (TLD) can compete on merit, not budget.
✅ GoDaddy achieves sustainable revenue without degrading user trust.
Key takeaway: Building intelligent ad systems isn’t just about monetization — it’s about aligning incentives between users, advertisers, and platforms through smarter modeling.
👉 Dive into the technical deep dive: Balancing Revenue and Relevance: Optimizing TLD Ad Rankings https://www.godaddy.com/resources/news/balancing-revenue-and-relevance-optimizing-tld-ad-rankings
Curious to hear your perspective — share your thoughts below!