Behind the Scenes of an Ads Prediction System — Bunmi Akinremi

Ads prediction systems use multiple layers of filtering and models to narrow down relevant ads from thousands to a handful that match user context

Key data tracked includes:

• User demographics (age, gender, location, language)
• Browsing history
• Ad-specific features (format, performance history)
• Contextual data
• Ad placement data

System components include:

• Initial related ads selection (simple models)
• Complex models for relevance filtering
• Auction system for final ad selection
• Online training and continuous model updates

Performance metrics tracked:

• Click-through rates
• Conversion rates
• Return on ad spend
• Engagement metrics
• Cost per acquisition

Ethical considerations:

• Data privacy compliance
• Transparency in model decisions
• Avoiding user manipulation
• Country-specific regulatory requirements

Technical challenges:

• Managing massive data volumes
• Balancing model training frequency
• Handling unbalanced datasets
• Maintaining efficiency and low latency
• Continuous monitoring and recalibration

Models must adapt to:

• New ads without historical data
• Changing user behaviors
• Platform-specific requirements
• Different advertising goals