High-quality representations are a core requirement for effective recommendation. In this work, we study the problem of LLM-based descriptor generation, i.e., keyphrase-like natural language item representation generation frameworks with minimal constraints on downstream applications. We propose AgenticTagger, a framework that queries LLMs for representing items with sequences of text descriptors. However, open-ended generation provides little control over the generation space, leading to high cardinality, low-performance descriptors that renders downstream modeling challenging. To this end, AgenticTagger features two core stages: (1) a vocabulary building stage where a set of hierarchical, low-cardinality, and high-quality descriptors is identified, and (2) a vocabulary assignment stage where LLMs assign in-vocabulary descriptors to items. To effectively and efficiently ground vocabulary in the item corpus of interest, we design a multi-agent reflection mechanism where an architect LLM iteratively refines the vocabulary guided by parallelized feedback from annotator LLMs that validates the vocabulary against item data. Experiments on public and private data show AgenticTagger brings consistent improvements across diverse recommendation scenarios, including generative and term-based retrieval, ranking, and controllability-oriented, critique-based recommendation.

翻译：高质量表征是有效推荐的核心要求。本研究探讨基于大语言模型（LLM）的描述符生成问题，即构建类关键短语的自然语言物品表征生成框架，其对下游应用的约束最小。我们提出AgenticTagger框架，通过查询LLM以文本描述符序列表示物品。然而，开放式生成对生成空间的控制有限，易导致描述符基数高、性能低，使下游建模面临挑战。为此，AgenticTagger包含两个核心阶段：（1）词汇构建阶段：识别一组层次化、低基数且高质量的描述符；（2）词汇分配阶段：LLM为物品分配词汇表内的描述符。为在目标物品语料库中有效且高效地建立词汇基础，我们设计了多智能体反思机制：架构师LLM在标注员LLM的并行反馈指导下迭代优化词汇表，标注员LLM通过物品数据验证词汇有效性。在公开及私有数据上的实验表明，AgenticTagger在多样化推荐场景中均带来持续改进，包括生成式与基于术语的检索、排序，以及面向可控性的基于批评的推荐。