AI 中文总结
该研究针对基于LLM的生成式推荐模型中SID丢失协同信号导致准确率受限的问题，提出个性化自然语言引导的框架，通过添加分层协同线索恢复信号，提升推荐准确率。
AI 中文摘要
让基于大语言模型（LLM）的生成式推荐模型变得更强、更具个性化，是通过自然语言与显式推理实现的广泛期待但仍未解决的目标。这类模型将推荐转化为自回归生成项目的语义-ID（SID），即一组离散代码的短元组，因此良好的推荐就简化为生成正确的SID。在这种设定下，模型对自身知识的表述能力很差，文本与SID标记处于未对齐的嵌入空间中，导致深度推理很少能转化为正确的SID，而启用显式“思考”往往没有增益甚至会产生负面影响。更深层的原因是紧凑的SID无法同时容纳内容与协同信号：二者相互竞争，协同信号会丢失，而SID预测错误会直接限制推荐准确率，昂贵的多轮训练几乎没有帮助，很少有方法尝试在推理时补充缺失的信号。我们因此提出一个由个性化自然语言引导的框架，在模型生成过程中添加分层协同线索，且不改变主干或重新训练SID；该框架不直接将语言映射到SID，而是用语言在协同模式与其受众之间建立可分析的链接，恢复SID缺失的协同信号，最终在推荐准确率上取得持续提升，在推理时将生成过程建立在协同结构之上，而非依赖显式推理或重新训练。
英文摘要
Making LLM-based generative recommendation models stronger and more personalized through natural language and explicit reasoning is a widely anticipated yet still unsolved goal. Such models cast recommendation as autoregressively generating an item's semantic-ID (SID), a short tuple of discrete codes, so that recommending well reduces to emitting the right SID. In this setting the model verbalizes its knowledge poorly, and text and SID tokens live in misaligned embedding spaces. Deep reasoning therefore rarely turns into a correct SID, and enabling explicit "thinking" often gives no gain or even hurts. The deeper cause is that a compact SID cannot hold content and collaborative signal at once: the two compete, and collaboration loses. Because a mis-predicted SID is a wrong recommendation, this caps accuracy directly. Costly multi-round training barely helps, and few methods try to supply the missing signal at inference time. What is missing is a reliable channel that carries collaborative signal into SID generation. We therefore propose a framework, guided by personalized natural language, that adds hierarchical collaborative cues as the model generates, without altering the backbone or retraining the SIDs. Rather than mapping language onto SIDs directly, it uses language to attach analyzable links between collaborative patterns and their audiences, restoring the collaborative signal that SIDs miss. The result is consistent gains in recommendation accuracy, grounding generation in collaborative structure at inference time rather than relying on explicit reasoning or retraining.
Comments8 pages, 4 figures
