User interactions on e-commerce platforms are inherently diverse, involving behaviors such as clicking, favoriting, adding to cart, and purchasing. The transitions between these behaviors offer valuable insights into user-item interactions, serving as a key signal for understanding evolving preferences. Consequently, there is growing interest in leveraging multi-behavior data to better capture user intent. Recent studies have explored sequential modeling of multi-behavior data, many relying on transformer-based architectures with polynomial time complexity. While effective, these approaches often incur high computational costs, limiting their applicability in large-scale industrial systems with long user sequences. To address this challenge, we propose the Transition-Aware Graph Attention Network (TGA), a linear-complexity approach for modeling multi-behavior transitions. Unlike traditional transformers that treat all behavior pairs equally, TGA constructs a structured sparse graph by identifying informative transitions from three perspectives: (a) item-level transitions, (b) category-level transitions, and (c) neighbor-level transitions. Built upon the structured graph, TGA employs a transition-aware graph Attention mechanism that jointly models user-item interactions and behavior transition types, enabling more accurate capture of sequential patterns while maintaining computational efficiency. Experiments show that TGA outperforms all state-of-the-art models while significantly reducing computational cost. Notably, TGA has been deployed in a large-scale industrial production environment, where it leads to impressive improvements in key business metrics.

翻译：电子商务平台上的用户交互本质上是多样化的，涉及点击、收藏、加购和购买等行为。这些行为之间的转移为用户-物品交互提供了宝贵的洞见，是理解用户偏好演变的关键信号。因此，利用多行为数据以更好地捕捉用户意图正受到越来越多的关注。近期研究探索了对多行为数据进行序列建模，其中许多依赖于具有多项式时间复杂度的基于Transformer的架构。这些方法虽然有效，但通常会产生高昂的计算成本，限制了其在具有长用户序列的大规模工业系统中的适用性。为应对这一挑战，我们提出了转移感知图注意力网络（TGA），一种用于建模多行为转移的线性复杂度方法。与将全部行为对同等对待的传统Transformer不同，TGA通过从三个视角识别信息性转移来构建结构化稀疏图：（a）物品级转移，（b）品类级转移，以及（c）邻居级转移。基于该结构化图，TGA采用一种转移感知图注意力机制，该机制联合建模用户-物品交互与行为转移类型，从而在保持计算效率的同时，更准确地捕捉序列模式。实验表明，TGA在显著降低计算成本的同时，性能优于所有最先进的模型。值得注意的是，TGA已部署于大规模工业生产环境，并在关键业务指标上带来了显著的提升。