Trinity解决新场景下大规模冷启动用户推荐难题
Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users
- 融合特征工程、模型架构与稳定更新机制
- 在十亿级用户迁移中显著提升推荐效果
- 适合新场景产品上线时的冷启动推荐
新场景中的早期用户加剧了冷启动问题，以往工作多仅通过模型架构部分缓解。新用户体验替代旧产品时面临行为信号稀疏、低活跃群体和模型性能不稳定等挑战。我们认为有效推荐需特征工程、模型架构与稳定更新三者协同。为此提出Trinity框架，从已有场景中提取有用信息，确保新场景下的预测有效性与准确性。本文展示Trinity在十亿级微软产品迁移中的应用，离线与在线实验均证明其在应对新用户新场景复合挑战上取得显著改进。
原文摘要 · Abstract (English)
Early-stage users in a new scenario intensify cold-start challenges, yet prior works often address only parts of the problem through model architecture. Launching a new user experience to replace an established product involves sparse behavioral signals, low-engagement cohorts, and unstable model performance. We argue that effective recommendations require the synergistic integration of feature engineering, model architecture, and stable model updating. We propose Trinity, a framework embodying this principle. Trinity extracts valuable information from existing scenarios while ensuring predictive effectiveness and accuracy in the new scenario. In this paper, we showcase Trinity applied to a billion-user Microsoft product transition. Both offline and online experiments demonstrate that our framework achieves substantial improvements in addressing the combined challenge of new users in new scenarios.
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