IDGenRec: LLM-RecSys Alignment with Textual ID Learning
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, Yongfeng Zhang
Abstract
LLM-based Generative recommendation has attracted significant attention. However, in contrast to standard NLP tasks that inherently operate on human vocabulary, current generative recommendation approaches struggle to effectively encode items within the text-to-text framework. Due to this issue, the true potential of LLM-based generative recommendation remains largely unexplored. To better align LLMs with recommendation needs, we propose IDGenRec, 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.