Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific information. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information. The multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that our HierRec outperforms existing models significantly.

翻译：点击率（CTR）预测是推荐系统和广告系统中的基础技术。近期研究表明，实施多场景推荐有助于加强信息共享并提升整体性能。然而，现有模型仅依赖人工先验规则预设的场景识别进行粗粒度显式场景建模，存在偏差且非最优。为解决这些局限，我们提出面向多场景推荐的情景感知分层动态网络（HierRec），该网络能够自适应感知隐式模式，并联合进行显式与隐式场景建模。具体而言，HierRec基于动态权重设计基础场景导向模块以捕获场景特定信息，进而提出分层显式与隐式情景感知模块以建模混合粒度的场景信息。多头隐式建模设计有助于从不同视角感知差异化模式。在两个公开数据集以及主流在线广告平台的实际工业应用中的实验表明，HierRec的性能显著优于现有模型。