DPGFlow: Decoupled Preference Guided Flow Matching for Cross Domain Sequential Recommendation

Abstract

Cross-Domain Sequential Recommendation (CDSR) aims to improve next-item prediction by leveraging users’ sequential behaviors across multiple domains. Despite recent progress, existing CDSR models suffer from two fundamental limitations: (1) they often entangle transferable, domain-invariant interests with domain-specific preferences, leading to negative transfer across heterogeneous domains; and (2) they are highly sensitive to noisy interactions such as misclicks and abrupt domain transitions. Generative models have recently emerged as a promising paradigm for modeling complex preference dynamics and mitigating noise. However, diffusion-based approaches rely on Gaussian initialization and stochastic denoising, resulting in unstable inference. Flow Matching (FM) offers a deterministic and efficient alternative by directly learning preference transport trajectories, yet existing FM-based recommenders are restricted to single-domain settings and fail to account for domain-dependent signals critical in CDSR. To bridge these gaps, we propose DPGFlow, the first Flow Matching framework specifically designed for CDSR. DPGFlow explicitly separates user preferences into domain-invariant and domain-specific components and injects them as structured guidance into a domain-aware conditional flow field. This design enables stable and efficient few-step inference and facilitates effective knowledge transfer under heterogeneous and noisy behaviors. Extensive experiments on multiple real-world CDSR benchmarks demonstrate that DPGFlow consistently outperforms state-of-the-art baselines, while exhibiting strong robustness under noise, cold-start, and domain-transition scenarios. The data and code are available at https://github.com/seanye93/DPGFlow.