Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space: 1) item indexing utilizes identifiers to represent items in the language space, and 2) generation grounding associates LLMs' generated token sequences to in-corpus items. However, previous methods exhibit inherent limitations in the two steps. Existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) either lose semantics or lack adequate distinctiveness. Moreover, prior generation grounding methods might generate invalid identifiers, thus misaligning with in-corpus items. To address these issues, we propose a novel Transition paradigm for LLM-based Recommender (named TransRec) to bridge items and language. Specifically, TransRec presents multi-facet identifiers, which simultaneously incorporate ID, title, and attribute for item indexing to pursue both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to ensure generating valid identifiers only and utilize substring indexing to encourage LLMs to generate from any position of identifiers. Lastly, TransRec presents an aggregated grounding module to leverage generated multi-facet identifiers to rank in-corpus items efficiently. We instantiate TransRec on two backbone models, BART-large and LLaMA-7B. Extensive results on three real-world datasets under diverse settings validate the superiority of TransRec.

翻译：利用大语言模型（LLMs）进行推荐正迅速兴起，其依赖于两个基本步骤来桥接推荐物品空间与语言空间：1）物品索引使用标识符在语言空间中表示物品；2）生成落地将LLMs生成的词元序列与语料库内物品关联。然而，先前方法在这两个步骤中存在固有局限。现有基于ID的标识符（如数字ID）和基于描述的标识符（如标题）要么丢失语义，要么缺乏足够的区分度。此外，先前的生成落地方法可能产生无效标识符，从而导致与语料库内物品失配。为解决这些问题，我们提出了一种新颖的基于LLM的推荐系统过渡范式（命名为TransRec）来桥接物品与语言。具体而言，TransRec提出多维度标识符，同时融合ID、标题和属性进行物品索引，以兼顾区分度与语义信息。此外，我们为TransRec设计了专用数据结构以确保仅生成有效标识符，并利用子串索引技术激励LLMs从标识符的任意位置开始生成。最后，TransRec提出聚合落地模块，利用生成的多维度标识符高效排序语料库内物品。我们在两个骨干模型BART-large和LLaMA-7B上实例化了TransRec。在三种现实数据集上多种设置下的广泛实验结果验证了TransRec的优越性。