# Multi-Behavior Sequential Modeling with Transition-Aware Graph Attention Network for E-Commerce Recommendation Source: [Multi-Behavior Sequential Modeling with Transition-Aware Graph Attention Network for E-Commerce Recommendation]() Collection index: [llms.txt]() Data updated: 2026-01-23T05:00:13.652Z Authors: Hanqi Jin, Gaoming Yang, Zhangming Chan, Yapeng Yuan, Longbin Li, Fei Sun, Yeqiu Yang, Jian Wu, Yuning Jiang, Bo Zheng Published: 2026-01-21T12:53:32.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [CVR Prediction on Taobao Dataset (test)](): CVR prediction; Taobao Dataset (test); AUC: 0.7454. - [CVR Prediction on Industrial Dataset (test)](): CVR prediction; Industrial Dataset (test); AUC: 0.8635. ## Original sources and implementation [Original paper]() [PDF]()