解决推荐系统冷启动问题，提升新内容曝光与用户参与度
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest
- 全链路多阶段优化，兼顾搜索与推荐场景
- 降低对旧内容偏好，提升新内容推荐准确率
- 支持快速实验与长期效果验证，适合大平台部署
本文提出一种新型解决方案，应对工业级搜索与推荐系统中的内容冷启动问题。相比以往方法，本方案具有三大创新：1）覆盖多阶段推荐链路，通用性强，适用于搜索与推荐双场景；2）缓解对已有内容的偏好偏差，提升各类内容的预测准确性，并减少大规模显式探索带来的短期收益损失；3）构建可扩展的评估框架，支持快速短期实验的同时验证长期影响。该系统已在 Pinterest 过去两年中持续迭代并成功上线，显著提升了新内容的探索率、整体用户参与度及内容生态健康度。
原文摘要 · Abstract (English)
In this paper, we propose a new solution for addressing the content cold-start problem in industry-scale search and recommender systems. Compared to prior approaches, we have made the following new contributions: 1) our solution spans the entire multi-stage funnel and generalizes well for both search and recommendation surfaces, 2) our solution reduces bias favoring existing content, allowing more accurate model prediction across content types and reducing short-term tradeoffs associated with high volumes of explicit content exploration, 3) our solution is evaluated with a scalable measurement framework that enables fast short-term experimentation while validating long-term impact. We have iteratively built and successfully deployed this new system at Pinterest in the past two years and observed significant improvements in fresh content exploration, overall user engagement, and content ecosystem health.
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