Hierarchical Conditional Transition Memory for Generative Recommendation

Generative recommendation (GR) with Semantic-ID (SID) represents each item as a short sequence of semantic tokens. These tokens introduce dual-role dependencies: fine-grained token transitions capture reusable structures shared across instances, whereas next-item selection demands dynamic contextual inference over user history. Standard dense Transformers entangle both roles within uniform computations, lacking an explicit retrieval pathway for recurrent structural patterns. Our empirical investigation reveals that existing GR models fail to fully exploit such reusable transitions, hindering their representational capacity. To address this, we propose Hierarchical Conditional Transition Memory (HCTM), which decouples generation into retrieval-augmented transition lookup and context-dependent preference inference. HCTM integrates an encoder-side Item-Internal Token Memory with a decoder-side Prefix-Conditioned Transition Memory, fusing retrieved memories into hidden states via context-conditioned residual gating. Extensive experiments across three benchmark datasets demonstrate that HCTM consistently outperforms competitive baselines. In-depth analyses further confirm that the performance gains stem directly from selective transition retrieval, while the memory substantially accelerates training convergence. The anonymized code repository is available at https://anonymous.4open.science/r/HCTM-C3B5/.