AI 中文总结
研究针对生成式推荐中语义标识符固定及端到端方法问题，提出Grevo框架，将SID分配当作可进化变量，基于单一多任务推荐器统一相关任务，用进化项索引重分配标识符，实验证明其性能优于现有生成式推荐方法。
AI 中文摘要
生成式推荐作为一种有前途的范式，将检索重新表述为对语义标识符（SIDs）的自回归生成。然而，SIDs通常在推荐器训练前由基于内容的分词器固定，导致重建内容与推荐器预测之间存在差距。近期的端到端方法通过联合训练分词器和推荐器来弥合差距，但存在标识符空间不稳定等问题。我们提出Grevo框架，将SID分配视为可进化离散变量，基于单一多任务推荐器统一行为SID生成和语义SID基础任务，通过进化项索引重新分配高风险标识符，实验表明Grevo优于现有方法。
英文摘要
Generative recommendation has recently emerged as a promising paradigm that reformulates retrieval as autoregressive generation over semantic identifiers (SIDs), achieving strong performance and drawing increasing attention as an alternative to matching. Despite this progress, SIDs are typically frozen by a content-based tokenizer before the recommender is trained, leaving a persistent gap between what best reconstructs an item's content and what a recommender can predict from user behavior. Recent end-to-end methods close this gap by jointly training the tokenizer and the recommender, but coupling the two destabilizes the identifier space and requires a second learnable model, alignment losses, and usually a delicate alternating-optimization schedule. To address this issue, we propose Grevo, a unified Generative recommendation framework with evolutionary item indexing, which treats the SID assignment itself as an evolvable discrete variable that adapts to behavioral feedback rather than as a tokenizer to be trained. Grevo builds on a single multitask recommender that unifies a behavioral SID generation task and a semantic SID grounding task, letting the recommender absorb the tokenizer's role. Through evolutionary item indexing, Grevo then uses the trained recommender itself as a posterior evaluator to reassign a budgeted set of high-risk identifiers under a fixed vocabulary and length. Together, these components turn index construction into a stable, feedback-driven search that requires no second learnable model, no alignment losses, and no alternating-optimization schedule. Extensive experiments on multiple real-world datasets demonstrate that Grevo consistently outperforms state-of-the-art generative recommendation methods.
