在工业约束下优化推荐系统特征交互，提升精度同时兼顾效率。
Improving feature interactions at Pinterest under industry constraints
- 针对延迟、显存、可复现性等工业限制，设计可落地的特征交互方案。
- 在真实场景中验证了改进方法能有效提升主页推荐效果。
- 为工程师提供特征交互架构选型的实验指导，实用性强。
在工业级推荐系统中，尽管已有大量新方法在基准数据集（如Criteo）上表现优异，但其实际应用常受限于模型延迟、GPU内存及可复现性等约束。本文基于Pinterest Homefeed排序模型，分享在这些限制下改进特征交互的经验。我们详细描述了遇到的具体挑战、采取的应对策略以及性能与可行性之间的权衡。此外，还设计了一系列学习实验，用于指导特征交互架构的选择。这些实践洞见对希望在真实系统中提升特征交互能力的工程师具有重要参考价值。
原文摘要 · Abstract (English)
Adopting advances in recommendation systems is often challenging in industrial settings due to unique constraints. This paper aims to highlight these constraints through the lens of feature interactions. Feature interactions are critical for accurately predicting user behavior in recommendation systems and online advertising. Despite numerous novel techniques showing superior performance on benchmark datasets like Criteo, their direct application in industrial settings is hindered by constraints such as model latency, GPU memory limitations and model reproducibility. In this paper, we share our learnings from improving feature interactions in Pinterest's Homefeed ranking model under such constraints. We provide details about the specific challenges encountered, the strategies employed to address them, and the trade-offs made to balance performance with practical limitations. Additionally, we present a set of learning experiments that help guide the feature interaction architecture selection. We believe these insights will be useful for engineers who are interested in improving their model through better feature interaction learning.
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