Multi-behavior recommendation aims to predict user conversions by modeling various interaction types that carry distinct intent signals. Recently, generative sequence modeling methods have emerged as an important paradigm for multi-behavior recommendation by achieving flexible sequence generation. However, existing generative methods typically treat behaviors as auxiliary token features and feed them into unified attention mechanisms. These models implicitly assume uniform activation of dependencies among historical behaviors, thereby failing to discern differences in intensity or capture transition patterns. To address these limitations, we propose BITRec, a novel generative multi-behavior recommendation framework that introduces structured behavioral modeling through selective dependency activation. BITRec incorporates (i) Hierarchical Behavior Aggregation (HBA), which explicitly models behavioral intensity differences through separated exploration and commitment pathways, and (ii) Transition Relation Encoding (TRE), which encodes transition structures through explicit learnable relation matrices. Experiments on four large-scale datasets (RetailRocket, Taobao, Tmall, Insurance Dataset) with millions of interactions achieve consistent improvements of 15-23% across multiple metrics, with peak gains of 22.79% MRR on Tmall and 17.83% HR@10, 17.55% NDCG@10 on Taobao.

翻译：多行为推荐旨在通过建模携带不同意图信号的多种交互类型来预测用户转化。近年来，生成式序列建模方法通过实现灵活的序列生成，已成为多行为推荐领域的重要范式。然而，现有生成方法通常将行为视为辅助词元特征并输入统一注意力机制。这些模型隐含假设历史行为间的依赖关系均匀激活，从而无法区分行为强度差异或捕捉转换模式。为解决上述局限，我们提出BITRec——一种通过选择性依赖激活实现结构化行为建模的新型生成式多行为推荐框架。BITRec包含：（i）层次化行为聚合模块（HBA），通过分离的探索路径与承诺路径显式建模行为强度差异；（ii）转换关系编码模块（TRE），通过显式可学习关系矩阵编码转换结构。在四个包含数百万交互的大规模数据集（RetailRocket、淘宝、天猫、保险数据集）上的实验表明，该方法在多项指标上实现15-23%的持续提升，其中在天猫数据集上MRR最高提升22.79%，在淘宝数据集上HR@10与NDCG@10分别提升17.83%与17.55%。