Users increasingly interact with content across multiple domains, resulting in sequential behaviors marked by frequent and complex transitions. While Cross-Domain Sequential Recommendation (CDSR) models two-domain interactions, Multi-Domain Sequential Recommendation (MDSR) introduces significantly more domain transitions, compounded by challenges such as domain heterogeneity and imbalance. Existing approaches often overlook the intricacies of domain transitions, tend to overfit to dense domains while underfitting sparse ones, and struggle to scale effectively as the number of domains increases. We propose \textit{GMFlowRec}, an efficient generative framework for MDSR that models domain-aware transition trajectories via Gaussian Mixture Flow Matching. GMFlowRec integrates: (1) a unified dual-masked Transformer to disentangle domain-invariant and domain-specific intents, (2) a Gaussian Mixture flow field to capture diverse behavioral patterns, and (3) a domain-aligned prior to support frequent and sparse transitions. Extensive experiments on JD and Amazon datasets demonstrate that GMFlowRec achieves state-of-the-art performance with up to 44\% improvement in NDCG@5, while maintaining high efficiency via a single unified backbone, making it scalable for real-world multi-domain sequential recommendation.

翻译：用户日益频繁地在多个领域内与内容进行交互，导致其行为序列呈现出频繁且复杂的跨域转移特征。虽然跨域序列推荐模型能够处理双域交互，但多域序列推荐场景引入了显著更多的域间转移，并伴随着域异构性与数据不平衡等挑战。现有方法往往忽视域间转移的复杂性，容易对稠密域过拟合而对稀疏域欠拟合，且难以随着域数量增加而有效扩展。我们提出 \textit{GMFlowRec}，一种面向多域序列推荐的高效生成式框架，通过高斯混合流匹配对域感知的转移轨迹进行建模。GMFlowRec 整合了三个核心组件：(1) 采用统一的双掩码 Transformer 来解耦域不变意图与域特定意图；(2) 构建高斯混合流场以捕捉多样化的行为模式；(3) 引入域对齐先验以支持频繁与稀疏的转移。在京东和亚马逊数据集上的大量实验表明，GMFlowRec 在 NDCG@5 指标上实现了高达 44\% 的性能提升，达到当前最优水平，同时通过单一统一的主干网络保持了高效率，使其能够扩展至现实世界的多域序列推荐场景。