  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.
