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Bridging the Gap: Diagnosing Online–Offline Discrepancy in Pinterest’s L1 Conversion Models | Endigest
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Pinterest Engineering Blog - Medium
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Machine Learning
Bridging the Gap: Diagnosing Online–Offline Discrepancy in Pinterest’s L1 Conversion Models
2026-02-27
10 min read
0
by Pinterest Engineering
Endigest AI Core Summary
Pinterest investigates the online–offline discrepancy in L1 CVR models in their ads funnel.
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New CVR models showed 20–45% LogMAE reduction offline but neutral or negative online A/B results.
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Offline bugs, exposure bias, and serving health were ruled out as root causes.
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Feature gap: models trained on rich signals (targeting flags, conversion counts, image embeddings) absent from L1 serving.
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Embedding version skew in two-tower models caused query and Pin checkpoint misalignment at serving.
Tags:
#ads-ranking
#machine-learning
#pinterest
#engineering
#conversion-modeling
Read Original (Pinterest Engineering Blog - Medium)