Multi-granularity Fatigue in Recommendation

Xie, Rong; Ling, Chen; Zhang, Shaoliang; Xia, Feng; Lin, Leyu

doi:10.1145/3511808.3557651

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…Several papers have proposed multi-armed bandit models where surrogate outcomes encode actions' longterm impacts. These include bandit models where poor recommendations cause attrition [Ben-Porat et al, 2022, Bastani et al, 2022 and bandit models where objectives incorporate diversity/boredom considerations [Xie et al, 2022, Cao et al, 2020, Ma et al, 2016. Wu et al [2017] studies a variation on typical bandit model where actions impact whether a user will return to the system.…”

Section: Surrogate Outcomes and Proxy-metricsmentioning

confidence: 99%

Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective

Maystre¹,

Russo²,

Yu³

2023

Preprint

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We study the problem of optimizing a recommender system for outcomes that occur over several weeks or months. We begin by drawing on reinforcement learning to formulate a comprehensive model of users' recurring relationships with a recommender system. Measurement, attribution, and coordination challenges complicate algorithm design. We describe careful modeling-including a new representation of user state and key conditional independence assumptions-which overcomes these challenges and leads to simple, testable recommender system prototypes. We apply our approach to a podcast recommender system that makes personalized recommendations to hundreds of millions of listeners. A/B tests demonstrate that purposefully optimizing for long-term outcomes leads to large performance gains over conventional approaches that optimize for short-term proxies.

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