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Pinterest paper introduces model-agnostic rewards for long-term user engagement

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework for optimizing long-term user engagement in large-scale recommendation systems. This model-agnostic approach identifies session-level behaviors that predict future retention, deriving multiple reward signals from observed user actions. The framework has been successfully deployed across various Pinterest surfaces, including Homefeed, Related Pins, Search, and Notifications, demonstrating consistent improvements in engagement and retention metrics. AI

IMPACT This framework could improve user retention and engagement across various platforms by optimizing recommendation algorithms for long-term value.

RANK_REASON The cluster contains an academic paper detailing a new framework for recommendation systems.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Pinterest paper introduces model-agnostic rewards for long-term user engagement

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The cluster contains an academic paper detailing a new framework for recommendation systems.
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2 independent sources
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paper, product
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52 days old
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COVERAGE [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 ·

    Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

    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 ·

    Long-term User Engagement Optimization through Model-agnostic Downstream Rewards Learning

    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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