AI 中文总结
研究针对生成式跨域推荐的异构模式建模挑战，提出HD-Rec框架，采用分层量化与领域自适应稀疏路由技术，在三个公开基准上优于各类基线方法。
AI 中文摘要
生成式推荐（GenRec）是一种极具前景的范式，它通过将物品编码为紧凑的语义ID（SIDs）并在各类推荐场景中通过下一个词元预测来建模用户行为，取得了显著的经验成功。将这一范式扩展到跨域推荐颇具挑战性，因为统一模型必须适配不同领域间异构的物品语义与行为模式。现有方法通常依赖全局共享表示或轻量级领域自适应，可能在不同语义粒度下建模异构模式的能力不足。为应对这些挑战，我们提出HD-Rec，一种用于跨域推荐的统一生成式框架。HD-Rec采用分层领域感知量化器，利用全局共享的粗粒度码本与自适应路由的细粒度码本构建语义标识符。它进一步引入领域自适应稀疏专家混合模块，该模块结合了持续激活的共享专家与动态选择的专用专家。为提升多词元物品表示的连贯性，我们开发了跨粒度路由一致性目标，将词元级路由决策正则化至其物品级共识。在三个公开跨域推荐基准上的实验表明，HD-Rec相较于竞争性的序列、生成式及跨域推荐基线均实现了持续提升。
英文摘要
Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user behavior via next-token prediction across diverse recommendation scenarios. Extending this paradigm to cross-domain recommendation is challenging because a unified model must accommodate heterogeneous item semantics and behavioral patterns across domains. Existing methods commonly rely on globally shared representations or lightweight domain adaptation, which may provide insufficient capacity for modeling heterogeneous patterns at different semantic granularities. To address these challenges, we propose HD-Rec, a unified generative framework for cross-domain recommendation. HD-Rec employs a hierarchical domain-aware quantizer that constructs semantic identifiers using globally shared coarse-level codebooks and adaptively routed fine-level codebooks. It further introduces a domain-adaptive sparse mixture-of-experts module that combines a continuously activated shared expert with a dynamically selected specialized expert. To improve the coherence of multi-token item representations, we develop a cross-granularity routing consistency objective that regularizes token-level routing decisions toward their item-level consensus. Experiments on three public cross-domain recommendation benchmarks show that HD-Rec consistently improves over competitive sequential, generative, and cross-domain recommendation baselines.
