用标准化关键词提升大模型推荐效果，解决幻觉与语义鸿沟问题。
Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers
- 提出术语标识符TID，用标准关键词代替文本或语义码作为物品标识
- GRLM框架在多个数据集上超越基线，性能显著提升
- 适合想构建通用、高精度生成式推荐系统的研究者
利用大语言模型（LLMs）的开放世界知识与理解能力，发展通用、语义感知的生成式推荐系统已成为重要研究方向。然而现有方法在构建物品标识符时面临瓶颈：基于文本的方法引入大模型庞大的输出空间，导致幻觉；基于语义标识符（SIDs）的方法存在SID与大模型原生词汇间的语义鸿沟，需昂贵的词汇扩展与对齐训练。为此，本文提出术语标识符（TIDs），即一组语义丰富且标准化的文本关键词，作为稳健的物品标识符。我们设计了以TIDs为核心的GRLM框架，通过上下文感知的术语生成将物品元数据转化为标准化TIDs，并采用整合指令微调协同优化术语内化与序列推荐。此外，设计弹性标识符锚定机制实现鲁棒的物品映射。在真实数据集上的大量实验表明，GRLM在多种场景下显著优于基线，指明了可泛化、高性能生成式推荐系统的可行方向。
原文摘要 · Abstract (English)
Leveraging the vast open-world knowledge and understanding capabilities of Large Language Models (LLMs) to develop general-purpose, semantically-aware recommender systems has emerged as a pivotal research direction in generative recommendation. However, existing methods face bottlenecks in constructing item identifiers. Text-based methods introduce LLMs' vast output space, leading to hallucination, while methods based on Semantic IDs (SIDs) encounter a semantic gap between SIDs and LLMs' native vocabulary, requiring costly vocabulary expansion and alignment training. To address this, this paper introduces Term IDs (TIDs), defined as a set of semantically rich and standardized textual keywords, to serve as robust item identifiers. We propose GRLM, a novel framework centered on TIDs, employs Context-aware Term Generation to convert item's metadata into standardized TIDs and utilizes Integrative Instruction Fine-tuning to collaboratively optimize term internalization and sequential recommendation. Additionally, Elastic Identifier Grounding is designed for robust item mapping. Extensive experiments on real-world datasets demonstrate that GRLM significantly outperforms baselines across multiple scenarios, pointing a promising direction for generalizable and high-performance generative recommendation systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。