让商品ID直接进入大模型，实现快速推荐且不降精度
Efficient Item ID Generation for Large-Scale LLM-based Recommendation
- 将商品ID作为单个令牌直接输入模型，避免复杂编码
- 在亚马逊数据集上召回率和NDCG均提升，推理速度加快5-14倍
- 适合需要低延迟实时推荐的工业场景
将商品目录与用户行为整合进大语言模型可增强推荐系统的常识理解能力，但现实中百万级离散商品标识符（Item ID）规模与模型通常使用的较小文本词表存在巨大矛盾。现有主流方法认为无法将商品ID作为第一类公民处理，必须将其分词为多个令牌，这导致服务时延高，难以满足实时低延迟需求。本文挑战这一共识，首次实现商品ID作为第一类公民直接融入模型。通过简单有效的训练与推理优化，支持商品的单令牌表示与单步解码。在Amazon购物数据集上，该方法在推荐质量（召回率与NDCG）上优于现有技术，同时推理效率提升5-14倍。本工作从效率角度开辟新方向，有望推动大模型推荐系统中标识符集成的研究。
原文摘要 · Abstract (English)
Integrating product catalogs and user behavior into LLMs can enhance recommendations with broad world knowledge, but the scale of real-world item catalogs, often containing millions of discrete item identifiers (Item IDs), poses a significant challenge. This contrasts with the smaller, tokenized text vocabularies typically used in LLMs. The predominant view within the LLM-based recommendation literature is that it is infeasible to treat item ids as a first class citizen in the LLM and instead some sort of tokenization of an item into multiple tokens is required. However, this creates a key practical bottleneck in serving these models for real-time low-latency applications. Our paper challenges this predominant practice and integrates item ids as first class citizens into the LLM. We provide simple, yet highly effective, novel training and inference modifications that enable single-token representations of items and single-step decoding. Our method shows improvements in recommendation quality (Recall and NDCG) over existing techniques on the Amazon shopping datasets while significantly improving inference efficiency by 5x-14x. Our work offers an efficiency perspective distinct from that of other popular approaches within LLM-based recommendation, potentially inspiring further research and opening up a new direction for integrating IDs into LLMs. Our code is available here https://drive.google.com/file/d/1cUMj37rV0Z1bCWMdhQ6i4q4eTRQLURtC
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