HoloRec：面向生成式推荐的整体编码与交错推理
HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation
AI总结：
提出HoloRec，通过多粒度嵌套残差量化构建层次语义编码矩阵，实现内生的思维链推理，无需外部标注，在稀疏场景下显著提升推荐准确率。
摘要：
将任务建模为序列生成的生成式推荐模型克服了传统级联架构的目标碎片化问题，但现有方法仍存在缺乏层次结构用于多步推理的扁平语义表示，以及需要昂贵标注且与生成目标脱节的外部构建思维链（CoT）等问题。我们提出HoloRec，一种内生的思维链推荐机制，通过多粒度嵌套残差量化构建层次语义编码矩阵，并由整体重建损失优化，统一了表示、推理和生成。HoloRec支持两种推理模式：非思考模式使用轻量级多粒度监督对齐进行快速预测，思考模式采用交错推理方案动态生成CoT步骤，将推理直接嵌入生成过程，无需外部数据。在多个公开推荐数据集上的实验表明，HoloRec持续优于基线，在稀疏场景下尤其显著，且思考模式在仅增加适度推理开销的情况下实现了比非思考模式更高的准确率。
英文摘要：
Generative recommendation models that formulate the task as sequence generation overcome the objective fragmentation problem of traditional cascade architectures, yet existing approaches still suffer from flat semantic representations lacking hierarchical structure for multi-step reasoning and an externally constructed chain-of-thought (CoT) that requires expensive annotations and remains disconnected from the generation objective. We propose HoloRec, an endogenous chain-of-thought recommendation mechanism that unifies representation, reasoning, and generation by constructing a hierarchical semantic encoding matrix via multi-granularity nested residual quantization optimized by a holistic reconstruction loss. HoloRec supports two inference modes: a non-thinking mode that uses lightweight multi-granularity supervised alignment for fast prediction, and a thinking mode that employs an interleaved reasoning scheme to generate CoT steps on the fly, directly embedding reasoning into the generation process without external data. Experiments on multiple public recommendation datasets demonstrate that HoloRec consistently outperforms baselines, with especially significant gains in sparse scenarios, and the thinking mode achieves better accuracy than the non-thinking mode with only modest inference overhead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
