通过多维度预训练提升广告排序嵌入表性能，显著优化点击与转化率。
Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking
- 采用多算法融合预训练，增强嵌入表表达能力。
- 上线后点击率提升2.60%，每千次展示成本降低1.34%。
- 设计CPU-GPU混合服务架构，突破显存限制，支持大规模部署。
大型嵌入表在现代推荐系统中至关重要，能有效捕捉不同实体间复杂交互关系。在将大型嵌入表引入Pinterest广告排序模型的过程中，我们不仅面临稀疏性和可扩展性等常见挑战，还遇到若干特定于自身场景的障碍。初始尝试从头训练大型嵌入表导致指标表现平庸。为此，我们提出一种新颖的多维度预训练方案，融合多种预训练算法，显著丰富了嵌入表内容，并带来显著性能提升。结果表明，该方法在点击率（CTR）和转化率（CVR）两个领域均实现明显改善。此外，我们设计了一种基于CPU-GPU混合架构的服务系统，克服了GPU显存限制，提升了系统可扩展性。该框架已部署于Pinterest广告系统，实现1.34%的在线CPC降低和2.60%的CTR提升，且端到端延迟保持不变。
原文摘要 · Abstract (English)
Large embedding tables are indispensable in modern recommendation systems, thanks to their ability to effectively capture and memorize intricate details of interactions among diverse entities. As we explore integrating large embedding tables into Pinterest's ads ranking models, we encountered not only common challenges such as sparsity and scalability, but also several obstacles unique to our context. Notably, our initial attempts to train large embedding tables from scratch resulted in neutral metrics. To tackle this, we introduced a novel multi-faceted pretraining scheme that incorporates multiple pretraining algorithms. This approach greatly enriched the embedding tables and resulted in significant performance improvements. As a result, the multi-faceted large embedding tables bring great performance gain on both the Click-Through Rate (CTR) and Conversion Rate (CVR) domains. Moreover, we designed a CPU-GPU hybrid serving infrastructure to overcome GPU memory limits and elevate the scalability. This framework has been deployed in the Pinterest Ads system and achieved 1.34% online CPC reduction and 2.60% CTR increase with neutral end-to-end latency change.
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