Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

TL;DR

This paper introduces HierRec, a novel hierarchical dynamic network that adaptively models explicit and implicit scenarios for improved multi-scenario CTR prediction, outperforming existing methods.

Contribution

The paper proposes a scenario-aware hierarchical dynamic network that jointly models explicit and implicit scenarios, addressing limitations of prior coarse-grained approaches.

Findings

HierRec significantly outperforms existing models on public datasets.

The multi-head implicit modeling captures diverse scenario patterns effectively.

Experiments on industrial data validate practical applicability.

Abstract

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.