发表机构
PinEqualizer: Full Funnel Content Exploration and Debiasing System at Pinterest（PinEqualizer：全流程内容探索与去偏系统）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对行业规模搜索和推荐系统的内容冷启动问题，提出 PinEqualizer 系统，跨越多阶段漏斗，减少偏差，通过可扩展框架评估。在 Pinterest 部署后，显著改善了新鲜内容探索、用户参与度和内容生态健康。
AI 中文摘要
在本文中，我们提出了一种新的解决方案，以解决行业规模的搜索和推荐系统中的内容冷启动问题。与先前方法相比，有以下新贡献：一是解决方案跨越整个多阶段漏斗，对搜索和推荐界面都有良好的通用性；二是减少对现有内容的偏好偏差，能更准确地跨内容类型进行模型预测，并减少与大量显性内容探索相关的短期权衡；三是通过可扩展测量框架评估，能快速进行短期实验并验证长期影响。过去两年在 Pinterest 迭代构建并成功部署该新系统，观察到新鲜内容探索、整体用户参与度和内容生态系统健康方面有显著改善。
英文摘要
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.
Comments10 pages, 2 figures. Accepted at KDD 2026
