🤖 AI Summary
This work addresses the underrepresentation and path misalignment of cold-start items in generative recommendation systems caused by static semantic ID assignment. To tackle this, the authors propose a three-stage dynamic optimization framework that identifies static ID allocation as a key performance bottleneck. By decoupling tokenization from the generation objective, the framework introduces intent-aware tokenization and counterfactual contrastive learning to construct a behavior-aligned pool of semantic ID candidates. It further incorporates frozen-backbone evaluation without retraining and dynamic weighted beam search to maintain multiple hypotheses and progressively refine semantic IDs. Evaluated on three Amazon benchmarks, the method significantly outperforms existing generative and sequential recommendation approaches, achieving notable improvements on cold-start metrics.
📝 Abstract
Generative recommendation advances item retrieval by reformulating it as autoregressive generation of Semantic IDs (SIDs), compact token sequences that encode item semantics. While SIDs offer a strong semantic prior, current SID-based methods assign each item a single static identifier through offline tokenization before sufficient user feedback is observed. For cold-start items, this one-shot commitment produces poorly discriminative codes, generating misaligned paths that remain unrefined because the associated tokens are rarely sampled during training. We identify this early static commitment, not model capacity, as the fundamental cold-start bottleneck in SID-based generative recommendation. To overcome this bottleneck and bridge the disjoint objectives of tokenization and generation, we propose DREAM (Dynamic Refinement of Early Assignment Mappings), a three-stage framework that resolves this flaw through progressive refinement. First, an intent-aware tokenizer rebuilds the SID space through counterfactual contrastive learning, generating a diverse pool of behavior-aligned candidates per cold-start item. Second, the frozen recommendation backbone serves as an evaluator, selecting the most reliable candidate based on multi-context user support without retraining. Third, a dynamic beam mechanism maintains multiple weighted SID hypotheses throughout training and inference, preventing premature collapse to a single assignment. Extensive experiments on three Amazon benchmarks show that DREAM substantially outperforms state-of-the-art generative and sequential baselines on cold-start metrics.