Generative recommendation based on Large Language Models (LLMs) have transformed the traditional ranking-based recommendation style into a text-to-text generation paradigm. However, in contrast to standard NLP tasks that inherently operate on human vocabulary, current research in generative recommendations struggles to effectively encode recommendation items within the text-to-text framework using concise yet meaningful ID representations. To better align LLMs with recommendation needs, we propose IDGen, representing each item as a unique, concise, semantically rich, platform-agnostic textual ID using human language tokens. This is achieved by training a textual ID generator alongside the LLM-based recommender, enabling seamless integration of personalized recommendations into natural language generation. Notably, as user history is expressed in natural language and decoupled from the original dataset, our approach suggests the potential for a foundational generative recommendation model. Experiments show that our framework consistently surpasses existing models in sequential recommendation under standard experimental setting. Then, we explore the possibility of training a foundation recommendation model with the proposed method on data collected from 19 different datasets and tested its recommendation performance on 6 unseen datasets across different platforms under a completely zero-shot setting. The results show that the zero-shot performance of the pre-trained foundation model is comparable to or even better than some traditional recommendation models based on supervised training, showing the potential of the IDGen paradigm serving as the foundation model for generative recommendation. Code and data are open-sourced at https://github.com/agiresearch/IDGenRec.

翻译：基于大型语言模型（LLM）的生成式推荐已从传统的排序式推荐范式转变为文本到文本的生成范式。然而，与天然基于人类词汇的标准自然语言处理任务不同，当前生成式推荐研究在文本到文本框架下，难以使用简洁且富有意义的ID表示有效编码推荐项目。为了更好地使LLM与推荐需求对齐，我们提出IDGen，该方法利用人类语言词汇为每个项目生成独特、简洁、语义丰富且平台无关的文本型ID。这是通过在基于LLM的推荐器旁联合训练一个文本型ID生成器实现的，从而可将个性化推荐无缝融入自然语言生成过程。值得注意的是，由于用户历史记录以自然语言表示并与原始数据集解耦，我们的方法展示了构建基础生成式推荐模型的潜力。实验表明，在标准实验设置下，我们的框架在序列推荐中持续超越现有模型。随后，我们探索了利用所提方法在19个不同数据集上收集的数据训练基础推荐模型，并在完全零样本设置下，针对6个未见数据集跨平台测试其推荐性能。结果显示，预训练基础模型的零样本性能可与甚至超越部分基于监督训练的传统推荐模型，展示了IDGen范式作为生成式推荐基础模型的潜力。代码与数据已在https://github.com/agiresearch/IDGenRec开源。