New Framework Boosts Long-Term User Engagement in Recommender Systems
Key takeaways
- Optimizing for long-term user engagement is crucial for sustainable product growth.
- This framework offers a model-agnostic way to learn retention-predictive reward signals.
- Early observable behaviors can effectively predict future user retention.
- Successful deployment on Pinterest demonstrates its practical applicability and benefits.
Who benefits
Summary
This paper introduces a model-agnostic framework for optimizing long-term user value in large-scale recommendation systems by learning downstream rewards. It identifies early observable behaviors predictive of future retention and derives reward signals from user action patterns across multiple sources.
Why it matters
Professionals in product development and data science can leverage this framework to move beyond short-term metrics and build more sustainable, engaging user experiences that drive long-term value.
How to implement this in your domain
- 1Analyze existing user data to identify early behavioral signals that correlate with long-term retention.
- 2Implement an offline screening framework to validate the predictive power of these early signals.
- 3Develop and integrate model-agnostic downstream reward signals into current recommendation algorithms.
- 4Conduct A/B tests to measure the impact of the new reward signals on engagement and retention metrics.
- 5Iteratively refine the reward learning process based on experimental results and user feedback.
Original post by 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
"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…"
View on XOriginally posted by 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 on X · view source
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