融合用户站内站外行为，提升广告推荐精准度
Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads
- 构建包含站内点击与站外转化的异构图，捕捉用户完整兴趣路径
- 引入TransRA模型并结合注意力微调，使知识图谱嵌入有效融入广告排序
- 在Pinterest广告系统中实现CTR提升2.69%、CPC降低1.34%，适合工业级推荐场景
图神经网络（GNN）已被广泛应用于工业推荐系统，如GraphSage、TwHIM、LiGNN等。这些工作基于用户平台内行为构建图结构，通过图模型学习节点嵌入。除了站内行为外，用户的站外转化数据对广告模型理解购物兴趣至关重要。为此，我们构建了一个大规模异构图，融合用户站内广告交互和授权的站外转化行为。进一步提出TransRA（基于锚点的TransR），一种新型知识图谱嵌入（KGE）模型，以更高效地将图嵌入集成至广告排序模型。然而，初期广告排序模型难以直接使用KGE，离线实验仅获微弱增益。为此，我们采用大ID嵌入表技术，并在广告排序模型中创新性地设计基于注意力的KGE微调方法。最终，在点击率（CTR）与转化率（CVR）预测模型中观察到显著提升。该框架已部署于Pinterest广告参与度模型，带来2.69%的CTR提升和1.34%的每成本点击量（CPC）降低。我们认为本文技术可推广至其他大规模工业模型。
原文摘要 · Abstract (English)
Graph Neural Networks (GNN) have been extensively applied to industry recommendation systems, as seen in models like GraphSage\cite{GraphSage}, TwHIM\cite{TwHIM}, LiGNN\cite{LiGNN} etc. In these works, graphs were constructed based on users' activities on the platforms, and various graph models were developed to effectively learn node embeddings. In addition to users' onsite activities, their offsite conversions are crucial for Ads models to capture their shopping interest. To better leverage offsite conversion data and explore the connection between onsite and offsite activities, we constructed a large-scale heterogeneous graph based on users' onsite ad interactions and opt-in offsite conversion activities. Furthermore, we introduced TransRA (TransR\cite{TransR} with Anchors), a novel Knowledge Graph Embedding (KGE) model, to more efficiently integrate graph embeddings into Ads ranking models. However, our Ads ranking models initially struggled to directly incorporate Knowledge Graph Embeddings (KGE), and only modest gains were observed during offline experiments. To address this challenge, we employed the Large ID Embedding Table technique and innovated an attention based KGE finetuning approach within the Ads ranking models. As a result, we observed a significant AUC lift in Click-Through Rate (CTR) and Conversion Rate (CVR) prediction models. Moreover, this framework has been deployed in Pinterest's Ads Engagement Model and contributed to $2.69\%$ CTR lift and $1.34\%$ CPC reduction. We believe the techniques presented in this paper can be leveraged by other large-scale industrial models.
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