论文概要
研究领域: ML 作者: Olafur Gudmundsson, Bo Zhao, Huayi Liao, Anna Kiyantseva, Sai Xiao, Heath Vinicombe, Mostafa Keikha, Luke DeLuccia, Zihao Chen, Junpeng Hou, Weijie Jiang, Bhawna Juneja, Andreanne Lemay, Wei-Ting Lin, Keyvan Moghadam, Jiaxing Qu, Zhiqing Rao, Zhihua Zhang 发布时间: 2026-07-24 arXiv: 2607.22518
中文摘要
本文提出一种新的解决方案来解决工业级搜索和推荐系统中的内容冷启动问题。与以往方法相比，本文做出了以下新贡献：1)解决方案覆盖整个多阶段漏斗，并能很好地泛化到搜索和推荐两种场景；2)解决方案减少了对现有内容的偏差，允许跨内容类型进行更准确的模型预测，并减少了与高容量显式内容探索相关的短期权衡；3)解决方案通过可扩展的测量框架进行评估，该框架支持快速短期实验同时验证长期影响。在过去两年中，本文迭代构建并成功在Pinterest部署了这一新系统，观察到在新内容探索、整体用户参与度和内容生态系统健康方面的显著改进。
原文摘要
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 ob…
— 自动采集于 2026-07-28
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