  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.
