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English(EN) Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

Pinterest论文介绍用于长期用户参与度的模型无关奖励

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个新的框架，用于优化大规模推荐系统中的长期用户参与度。这种模型无关的方法识别预测未来留存的会话级行为，并从观察到的用户操作中导出多个奖励信号。该框架已成功部署到Pinterest的各种表面，包括Homefeed、Related Pins、Search和Notifications，在参与度和留存率指标方面均显示出持续的改进。 AI

影响 该框架通过优化推荐算法以实现长期价值，可以提高跨各种平台的用户的留存率和参与度。

排序理由 该集群包含一篇详细介绍推荐系统新框架的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Pinterest论文介绍用于长期用户参与度的模型无关奖励

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Research
该集群包含一篇详细介绍推荐系统新框架的学术论文。
Source corroboration
2 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, product
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
52 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dingsu Wang, Filip Ryzner, Kelly He, Armando Ordorica, David Woo, Aditya Mantha, Liyao Lu, Usha Amrutha Nookala, Haoran Guo, Jiacong He, Olafur Gudmundsson, Matt Chun, Krystal Benitez, Dhruvil Deven Badani, Yijie Dylan Wang ·

    通过模型无关的下游奖励学习实现长期用户参与度优化

    arXiv:2607.14192v1 Announce Type: new Abstract: As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention. However, dire…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yijie Dylan Wang ·

    通过模型无关的下游奖励学习实现长期用户参与度优化

    As recommender systems mature in the past few years, their optimization objectives have evolved from a primary focusing on short-term behavioral signals to a broader emphasis on long-term user engagement and retention. However, directly optimizing retention is difficult because r…

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