统一点击率预测的特征交互模型，提升广告系统精准度。
Towards Unifying Feature Interaction Models for Click-Through Rate Prediction
- 构建IPA框架，通过交互函数、层聚合与池化统一现有模型
- 在腾讯广告平台上线后点击率显著提升，实现GMV增长
- 适合从事推荐系统与广告点击率建模的研究者与工程师
特征交互建模在广告系统的点击率预测中至关重要。现有模型多采用矩阵分解将特征表示为低维嵌入向量，通过嵌入乘积建模交互关系。本文提出通用框架IPA，包含三个核心组件：交互函数用于特征交互，层池化构建高层交互，层聚合融合各层输出供分类器使用。我们证明多数现有模型可通过特定配置归入该框架。通过大量实验与维度坍塌分析评估不同配置表现，并基于最优组件设计新模型，在性能上达到当前领先水平。该模型在腾讯广告平台的在线A/B测试中带来显著GMV提升，已在多个核心场景部署为生产模型。
原文摘要 · Abstract (English)
Modeling feature interactions plays a crucial role in accurately predicting click-through rates (CTR) in advertising systems. To capture the intricate patterns of interaction, many existing models employ matrix-factorization techniques to represent features as lower-dimensional embedding vectors, enabling the modeling of interactions as products between these embeddings. In this paper, we propose a general framework called IPA to systematically unify these models. Our framework comprises three key components: the Interaction Function, which facilitates feature interaction; the Layer Pooling, which constructs higher-level interaction layers; and the Layer Aggregator, which combines the outputs of all layers to serve as input for the subsequent classifier. We demonstrate that most existing models can be categorized within our framework by making specific choices for these three components. Through extensive experiments and a dimensional collapse analysis, we evaluate the performance of these choices. Furthermore, by leveraging the most powerful components within our framework, we introduce a novel model that achieves competitive results compared to state-of-the-art CTR models. PFL gets significant GMV lift during online A/B test in Tencent's advertising platform and has been deployed as the production model in several primary scenarios.
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