Research paper

User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems

Research from GooglePublished by Association for Computational LinguisticsJul 2025PDF

Study how feedback from users can guide exploration in a large recommendation system.

User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems cover

Resource access

Get the full resource

Enter your business details to read the full resource.

First nameLast nameWork emailCompanyJob titleCountry or region

Jianling Wang, Yifan Liu, Yinghao Sun, Xuejian Ma, Yueqi Wang, He Ma, Zhengyang Su, Minmin Chen, Mingyan Gao, Onkar Dalal, Ed H. Chi, Lichan Hong, Ningren Han, Haokai Lu. User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track). © 2025 Association for Computational Linguistics. CC BY 4.0. 10.18653/v1/2025.acl-industry.70. Original PDF, unmodified.

CC BY 4.0

User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems

Related research.

Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning

The 2026 AI Index Report

Unleashing the Power of IBM watsonx.ai