DCN²通过显式建模跨层相似性，提升推荐系统精度与效率。
DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation
- 引入显式交叉层模拟FFM行为，增强特征交互建模能力
- 通过可学习权重缓解跨层信息丢失，提升特征表达精度
- 在超大规模推荐系统中实现每秒0.5亿次预测，优于原版DCNv2
Deep and Cross架构（DCNv2）是众多实际推荐系统中的稳定生产基线，其高效性与建模交互能力使其在复杂度远低于深度因子分解机（Deep FFM）的情况下仍具竞争力。本文提出对DCNv2的三项关键改进，构建出名为DCN²的新架构，已在实时推荐系统中部署，支持超过每秒0.5亿次预测，覆盖多种业务场景，在离线与在线实验中均超越DCNv2表现。改进包括：解决交叉层中的信息损失问题，通过可学习的查询级权重隐式管理特征碰撞，以及设计专用层显式建模成对相似性以模仿FFM行为。此外，DCN²在四个公开基准数据集上也展现出优异性能。
原文摘要 · Abstract (English)
The Deep and Cross architecture (DCNv2) is a robust production baseline and is integral to numerous real-life recommender systems. Its inherent efficiency and ability to model interactions often result in models that are both simpler and highly competitive compared to more computationally demanding alternatives, such as Deep FFMs. In this work, we introduce three significant algorithmic improvements to the DCNv2 architecture, detailing their formulation and behavior at scale. The enhanced architecture we refer to as DCN^2 is actively used in a live recommender system, processing over 0.5 billion predictions per second across diverse use cases where it out-performed DCNv2, both offline and online (ab tests). These improvements effectively address key limitations observed in the DCNv2, including information loss in Cross layers, implicit management of collisions through learnable lookup-level weights, and explicit modeling of pairwise similarities with a custom layer that emulates FFMs' behavior. The superior performance of DCN^2 is also demonstrated on four publicly available benchmark data sets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。