Recommendation foundation model utilizes large language models (LLM) for recommendation by converting recommendation tasks into natural language tasks. It enables generative recommendation which directly generates the item(s) to recommend rather than calculating a ranking score for each and every candidate item in traditional recommendation models, simplifying the recommendation pipeline from multi-stage filtering to single-stage filtering. To avoid generating excessively long text when deciding which item(s) to recommend, creating LLM-compatible item IDs is essential for recommendation foundation models. In this study, we systematically examine the item indexing problem for recommendation foundation models, using P5 as the representative backbone model and replicating its results with various indexing methods. To emphasize the importance of item indexing, we first discuss the issues of several trivial item indexing methods, such as independent indexing, title indexing, and random indexing. We then propose four simple yet effective solutions, including sequential indexing, collaborative indexing, semantic (content-based) indexing, and hybrid indexing. Our reproducibility study of P5 highlights the significant influence of item indexing methods on the model performance, and our results on real-world datasets validate the effectiveness of our proposed solutions.

翻译：推荐基础模型通过将推荐任务转化为自然语言任务，利用大语言模型（LLM）实现推荐功能。它采用生成式推荐范式，直接生成待推荐物品，而非像传统推荐模型那样逐一计算每个候选物品的排序分数，从而将推荐流程从多阶段筛选简化为单阶段筛选。为避免在决策推荐物品时生成过长的文本，创建与LLM兼容的物品ID对推荐基础模型至关重要。本研究以P5为代表性骨干模型，通过复现不同索引方法的实验结果，系统性地探讨了推荐基础模型的物品索引问题。为强调物品索引的重要性，我们首先分析了几种简单物品索引方法（如独立索引、标题索引和随机索引）的缺陷，进而提出了四种简洁有效的解决方案：顺序索引、协同索引、语义（基于内容）索引和混合索引。基于P5的可复现性研究揭示了物品索引方法对模型性能的显著影响，而真实数据集上的实验结果验证了所提方案的有效性。