From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest

2026-04-27 · Source: Pinterest Engineering Blog - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, E-commerce & Digital Commerce · Depth: Advanced, medium

Summary

Pinterest developed and refined a dedicated shopping conversion candidate generation model to optimize for offsite conversion events, which are sparser and noisier than onsite engagement signals. Launched in 2023, the initial model achieved a 2.3% increase in shopping conversion volume and a 1.5% higher clickthrough rate. Further iterations in 2025 introduced a unified multi-task architecture and an advertiser-level loss function, leading to a 3.1% improvement in Return on Ad Spend for US shopping campaigns. Key technical designs include a multi-surface model, dual positive signals (conversions and click duration-weighted engagement), and negative sampling. The architecture evolved from a sequential DCN v2 and MLP to a parallel design, yielding an 11% gain in offline recall@1000, and from a multi-head to a unified multi-task approach, boosting recall@100 by 42% for conversion tasks. This system serves over 600 million monthly active users.

Key takeaway

For AI Architects designing large-scale recommendation systems, you should prioritize robust data strategies for sparse signals. Implement multi-task learning with weighted auxiliary objectives to stabilize training and improve generalization. Consider parallelizing deep and cross-network components in your retrieval models to capture richer feature interactions, especially when optimizing for high-value, low-frequency events like offsite conversions. This approach can significantly boost conversion metrics and advertiser RoAS.

Key insights

Optimizing for sparse offsite conversions requires multi-task learning, robust data design, and parallel architectural components.

Principles

• Combine sparse conversion data with weighted engagement signals.
• Train a single model across multiple surfaces for data efficiency.
• Parallelize cross-network and deep network learning for richer interactions.

Method

The model uses a two-tower retrieval architecture with DCN v2 and parallel MLP cross layers, optimized via a unified multi-task loss function incorporating advertiser-level signals.

In practice

• Implement click duration-based re-weighting for engagement data.
• Use advertiser-level loss to stabilize sparse conversion signals.
• Adopt parallel DCN v2 and MLP for enhanced feature interaction learning.

Topics

• Shopping Conversion
• Candidate Generation
• Two-tower Models
• DCN v2
• Multi-task Learning
• Feature Engineering
• Pinterest Ads

Best for: Machine Learning Engineer, AI Architect, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Pinterest Engineering Blog - Medium.