大模型统一处理推荐系统多种召回任务，毫秒级响应且效果更优。
Large Language Model as Universal Retriever in Industrial-Scale Recommender System
- 用多查询表示建模复杂用户-物品关系，实现通用召回。
- 矩阵分解提升模型学习与泛化能力，概率采样降低计算开销。
- 可从数千万候选中自适应生成结果，适合工业级推荐场景。
在现实推荐系统中，不同召回目标通常依赖特定数据集和定制模型架构。本文证明大型语言模型（LLM）可在生成式召回框架下充当通用召回器。为建模生成式召回中的复杂用户-物品关系，提出多查询表示；针对工业级推荐中候选集极大规模的问题，引入矩阵分解以增强模型的学习能力、区分度与迁移性，并结合概率采样降低计算成本。最终的通用召回模型（URM）可根据任意给定目标，从数千万候选中自适应生成结果，延迟保持在数十毫秒内。在工业级数据上，URM在离线实验中超越了为不同召回目标专门设计的专家模型，在线上广告平台核心指标上提升了3%。
原文摘要 · Abstract (English)
In real-world recommender systems, different retrieval objectives are typically addressed using task-specific datasets with carefully designed model architectures. We demonstrate that Large Language Models (LLMs) can function as universal retrievers, capable of handling multiple objectives within a generative retrieval framework. To model complex user-item relationships within generative retrieval, we propose multi-query representation. To address the challenge of extremely large candidate sets in industrial recommender systems, we introduce matrix decomposition to boost model learnability, discriminability, and transferability, and we incorporate probabilistic sampling to reduce computation costs. Finally, our Universal Retrieval Model (URM) can adaptively generate a set from tens of millions of candidates based on arbitrary given objective while keeping the latency within tens of milliseconds. Applied to industrial-scale data, URM outperforms expert models elaborately designed for different retrieval objectives on offline experiments and significantly improves the core metric of online advertising platform by $3\%$.
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