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.

翻译：在工业环境中，由于独特的约束条件，采用推荐系统的先进技术往往具有挑战性。本文旨在通过特征交互的视角来阐明这些约束。特征交互对于在推荐系统和在线广告中准确预测用户行为至关重要。尽管许多新颖技术在Criteo等基准数据集上展现出优越性能，但其在工业环境中的直接应用受到模型延迟、GPU内存限制和模型可复现性等约束的阻碍。本文分享了我们在上述约束下改进Pinterest首页信息流排序模型中特征交互的经验。我们详细阐述了遇到的具体挑战、为解决这些挑战所采用的策略，以及在性能与实际限制之间进行权衡的取舍。此外，我们展示了一系列有助于指导特征交互架构选择的实验研究。我们相信这些见解将对希望通过改进特征交互学习来优化模型的工程师有所裨益。