分离用户与商品表征，提升Pinterest广告推荐精准度
Decoupled Entity Representation Learning for Pinterest Ads Ranking
- 上游训练多源数据，生成可复用的用户和商品嵌入
- 下游任务中使用预计算嵌入，显著提升点击率与转化率
- 适合大规模推荐系统优化，尤其关注广告排序场景
本文提出一种基于上下游范式的新型框架，从多元数据源构建用户与商品（Pin）嵌入，以有效支持Pinterest个性化推荐。上游模型利用复杂架构在海量数据上训练，捕捉用户与商品间的复杂关系；为保障可扩展性，采用定期更新而非实时计算的嵌入方式，实现上下游模型异步协作。这些嵌入作为输入特征，被应用于多个下游任务，包括广告检索与点击率（CTR）、转化率（CVR）预测模型。实验表明，该框架在离线与在线评估中均显著提升各类下游任务表现，已在Pinterest生产广告排序系统中部署，带来可观的线上指标增长。
原文摘要 · Abstract (English)
In this paper, we introduce a novel framework following an upstream-downstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential for Pinterest to deliver personalized Pins and ads effectively. Our upstream models are trained on extensive data sources featuring varied signals, utilizing complex architectures to capture intricate relationships between users and Pins on Pinterest. To ensure scalability of the upstream models, entity embeddings are learned, and regularly refreshed, rather than real-time computation, allowing for asynchronous interaction between the upstream and downstream models. These embeddings are then integrated as input features in numerous downstream tasks, including ad retrieval and ranking models for CTR and CVR predictions. We demonstrate that our framework achieves notable performance improvements in both offline and online settings across various downstream tasks. This framework has been deployed in Pinterest's production ad ranking systems, resulting in significant gains in online metrics.
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