CogRec：用于生成式推荐的结构认知快速与慢速推理
CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation
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中文总结 AI 辅助
研究基于语义ID的生成式推荐问题，提出CogRec框架，通过增强SID层次结构、引入SID路由操作进行推理，实验表明该框架能改进直接生成，特定情况下结构基础推理有效，长或弱支持路由有弊端。
中文摘要 AI 辅助
基于语义ID的生成式推荐将每个项目表示为分层离散令牌序列，并将下一个项目预测重新表述为受限序列生成。现有方法主要将语义ID用作要记忆的目标序列，未充分利用层次结构、层内关系和项目邻域作为显式推理空间。我们提出CogRec，一种结构认知快速与慢速推理框架，将中间推理基于用于目标生成的相同SID拓扑。CogRec用层内语义图和项目级邻域增强垂直SID层次结构，并引入SID路由通过逐层匹配、横向跳跃和探索操作来表示推荐推理。实验表明SID路由改进了相应的直接生成，结构基础推理在特定情况下最有用，而长或弱支持的路由会引入额外解码成本和累积错误。
英文摘要
Semantic-ID-based generative recommendation represents each item as a hierarchical discrete token sequence and reformulates next-item prediction as constrained sequence generation. Existing methods, however, mainly use Semantic IDs as target sequences to be memorized, leaving the hierarchy, intra-layer relations, and item neighborhoods underused as an explicit reasoning space. Explicit reasoning-enhanced generative methods often produce a natural-language rationale before the item identifier, but this rationale is only weakly coupled with the discrete SID space in which the final prediction is made. We propose CogRec, a structure-cognitive fast-and-slow reasoning framework that grounds intermediate reasoning in the same SID topology used for target generation. CogRec augments the vertical SID hierarchy with intra-layer semantic graphs and item-level neighborhoods, and introduces SID Routing to represent recommendation reasoning through layer-wise Match, LateralJump, and Explore operations. Exact matching implements fast semantic localization, whereas lateral and exploratory operations instantiate slower structural navigation. A supervised multi-stage pipeline aligns the newly introduced SID tokens, establishes direct SID generation, and trains natural-language and SID-routing reasoning branches from a shared checkpoint under the same trie-constrained output space. Experiments on three public sequential-recommendation benchmarks show that SID Routing improves its corresponding direct-generation, indicate that structure-grounded reasoning is most useful when prefix matching is insufficient but learnable SID-space transitions remain available, whereas long or weakly supported routes introduce additional decoding cost and accumulated errors. Code is available at https://github.com/caskcsg/CogRec
