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.

该研究使用大型语言模型将推荐任务转换为自然语言任务，提出了生成式推荐的基础模型，重点研究了指数化问题，提出了四种简单而有效的解决方案，并通过真实世界数据验证了提出解决方案的有效性。