Large Language Models (LLMs) have garnered considerable attention in recommender systems. To achieve LLM-based recommendation, item indexing and generation grounding are two essential steps, bridging between recommendation items and natural language. Item indexing assigns a unique identifier to represent each item in natural language, and generation grounding grounds the generated token sequences to in-corpus items. However, previous works suffer from inherent limitations in the two steps. For item indexing, existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) often compromise semantic richness or uniqueness. Moreover, generation grounding might inadvertently produce out-of-corpus identifiers. Worse still, autoregressive generation heavily relies on the initial token's quality. To combat these issues, we propose a novel multi-facet paradigm, namely TransRec, to bridge the LLMs to recommendation. Specifically, TransRec employs multi-facet identifiers that incorporate ID, title, and attribute, achieving both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to guarantee the in-corpus identifier generation and adopt substring indexing to encourage LLMs to generate from any position. We implement TransRec on two backbone LLMs, i.e., BART-large and LLaMA-7B. Empirical results on three real-world datasets under diverse settings (e.g., full training and few-shot training with warm- and cold-start testings) attest to the superiority of TransRec.

翻译：大语言模型在推荐系统中引起了广泛关注。为实现基于大语言模型的推荐，物品索引和生成落地是两个关键步骤，用于连接推荐物品与自然语言。物品索引为每个物品分配一个自然语言形式的唯一标识符，而生成落地则将生成的token序列映射到语料库内的物品。然而，现有方法在这两个步骤中存在固有局限性。在物品索引方面，现有的基于ID的标识符（如数字ID）和基于描述的标识符（如标题）往往在语义丰富性或唯一性上有所妥协。此外，生成落地可能意外产生语料库外的标识符。更严重的是，自回归生成高度依赖初始token的质量。为解决这些问题，我们提出了一种新颖的多面范式TransRec，用于连接大语言模型与推荐系统。具体而言，TransRec采用融合ID、标题和属性的多面标识符，同时实现区分性和语义性。此外，我们为TransRec引入了一种专门的数据结构，以确保生成语料库内的标识符，并采用子串索引技术，鼓励大语言模型从任意位置开始生成。我们在两种骨干大语言模型（即BART-large和LLaMA-7B）上实现了TransRec。在三个真实数据集上的实验结果（包括全量训练和少样本训练的热启动与冷启动测试）证明了TransRec的优越性。