Product bundling boosts e-commerce revenue by recommending complementary item combinations. However, existing methods face two critical challenges: (1) collaborative filtering approaches struggle with cold-start items owing to dependency on historical interactions, and (2) LLMs lack inherent capability to model interactive graph directly. To bridge this gap, we propose a dual-enhancement method that integrates interactive graph learning and LLM-based semantic understanding for product bundling. Our method introduces a graph-to-text paradigm, which leverages a Dynamic Concept Binding Mechanism (DCBM) to translate graph structures into natural language prompts. The DCBM plays a critical role in aligning domain-specific entities with LLM tokenization, enabling effective comprehension of combinatorial constraints. Experiments on three benchmarks (POG, POG_dense, Steam) demonstrate 6.3%-26.5% improvements over state-of-the-art baselines.

翻译：产品捆绑通过推荐互补商品组合提升了电商收入。然而，现有方法面临两个关键挑战：(1) 协同过滤方法因依赖历史交互数据，难以处理冷启动商品；(2) 大语言模型（LLM）缺乏直接建模交互图的固有能力。为弥合这一差距，我们提出一种双增强方法，将交互图学习与基于LLM的语义理解相结合用于产品捆绑。该方法引入图到文本范式，通过动态概念绑定机制（DCBM）将图结构转化为自然语言提示。DCBM在将领域特定实体与LLM分词对齐中发挥关键作用，从而有效理解组合约束。在三个基准数据集（POG、POG_dense、Steam）上的实验表明，该方法相较当前最优基线取得了6.3%-26.5%的性能提升。