CogRec: Structure-Cognitive Fast-and-Slow Reasoning for Generative Recommendation
2026-07-27 • Information Retrieval
Information Retrieval
AI summaryⓘ
The authors introduce CogRec, a recommendation method that treats items as sequences of special tokens called Semantic IDs (SIDs) organized in a hierarchy and graphs. Unlike past methods that mainly memorize these sequences, CogRec explicitly reasons within this structured space using operations like matching and navigating between layers to better predict the next item. Their approach combines quick exact matches with slower, more exploratory reasoning, improving recommendation accuracy when simple prefix matches are not enough. Experiments on public datasets show these structured reasoning steps help but can add complexity if overused.
Semantic IDhierarchical token sequencenext-item predictionsequential recommendationexplicit reasoningSID Routinggraph navigationmulti-stage trainingtrie-constrained decoding
Authors
Xiang Liu, Jingsong Su, Shuqi Zhao, Pengbo Mo, Yiming Qiu, Huimu Wang, Mingming Li, Jiao Dai, Jizhong Han, Songlin Hu
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