用大模型提升推荐系统探索能力，兼顾新颖与用户偏好。
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems
- 分拆大模型：一个专注新颖性，一个对齐用户偏好
- 通过最佳筛选机制，提升推荐相关性与多样性
- 适合需要平衡创新与用户满意度的推荐系统研发
在大规模推荐系统中，探索（即突破用户既有偏好）因反馈循环和探索行为信号不足而困难。大语言模型（LLMs）凭借其世界知识，可推荐超出反馈圈的内容。核心挑战在于如何在保持模型知识与推理能力的同时，使其与用户偏好对齐。本文提出一种结合分层规划与推理时缩放的新方法，旨在提升推荐相关性而不牺牲新颖性。我们分离新颖性与用户对齐目标，分别训练两个独立的LLM；随后对新颖性模型进行推理缩放，并由用户对齐模型从多个候选结果中选出最优项。线上实验显示，该方法显著提升了用户满意度（以观看活跃度和活跃用户数衡量）和探索多样性。
原文摘要 · Abstract (English)
Exploration, the act of broadening user experiences beyond their established preferences, is challenging in large-scale recommendation systems due to feedback loops and limited signals on user exploration patterns. Large Language Models (LLMs) offer potential solutions by leveraging their world knowledge to recommend novel content outside these loops. A key challenge is aligning LLMs with user preferences while preserving their knowledge and reasoning. To enhance planning for new user interests using LLMs, this paper introduces a novel approach that combines hierarchical planning with LLM inference-time scaling. This method aims to improve recommendation relevancy without compromising novelty. We decouple novelty and user-alignment, training separate LLMs for each objective. We then scale up the novelty-focused LLM's inference and select the best-of-n predictions using the user-aligned LLM. Live experiments demonstrate efficacy, showing significant gains in both user satisfaction (measured by watch activity and active user counts) and exploration diversity.
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