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 this https URL

通过 IDGen 方法将每个项目表示为独特、简洁、语义丰富、与平台无关的文本 ID，并通过基于 LLM 的推荐系统和文本 ID 生成器的协同训练，无缝地将个性化推荐融入自然语言生成，从而建立了一个基础生成推荐模型。实验结果表明，该方法在标准实验设置下在序列推荐方面始终优于现有模型，并且该方法在从 19 个不同数据集中收集的数据上进行零 - shot 训练后，在 6 个不同平台上的未见数据集上的推荐性能与一些基于监督训练的传统推荐模型相当甚至更好，展示了 IDGen 模式作为生成推荐基础模型的潜力。