arXiv Machine Learning July 17, 2026 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

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

Read the original on arXiv Machine 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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