用混合高斯流建模多领域行为轨迹，提升推荐精度与泛化能力
Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation
- 通过混合高斯流匹配捕捉复杂领域间行为模式
- 在京东和亚马逊数据集上NDCG@5提升最高达44%
- 适合处理稀疏领域且支持高效扩展的多领域推荐场景
用户越来越多地跨多个领域进行内容交互，导致序列行为中频繁且复杂的领域切换。现有跨领域推荐模型主要处理双领域交互，而多领域推荐（MDSR）引入了更多领域转换，面临领域异质性与分布不均等挑战。现有方法常忽略领域转换细节，易过拟合密集领域而忽略稀疏领域，且难以随领域数量增加而扩展。本文提出GMFlowRec，一种高效的生成式多领域推荐框架，通过高斯混合流匹配建模领域感知的转换轨迹。该方法包含：(1) 统一双掩码Transformer，分离领域无关与特定意图；(2) 高斯混合流场，捕获多样化行为模式；(3) 领域对齐先验，支持频繁与稀疏领域的转换。在京东和亚马逊数据集上的实验表明，GMFlowRec在NDCG@5上达到最先进性能，最多提升44%，同时凭借单一统一主干保持高效率，适用于真实世界的多领域推荐场景。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。