CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation

• URL: http://arxiv.org/abs/2607.24402v1
• Date: Mon, 27 Jul 2026 13:19:55 GMT
• Title: CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation
• Abstract summary: CogRec is a structure-cognitive fast-and-slow reasoning framework.<n>CogRec augments the vertical SID hierarchy with intra-layer semantic graphs and item-level neighborhoods.<n> SID Routing represents recommendation reasoning through layer-wise Match, LateralJump, and Explore operations.
• Score: 29.663400191042996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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