# Gaussian Mixture Flow Matching with Domain Alignment for Multi-Domain Sequential Recommendation

arXiv: https://arxiv.org/abs/2510.21021 · HTML: https://mlsanity.com/paper/2510.21021/

## Key facts

- Cited by 1 papers in the map (data through 13 Oct 2026).
- Placed in Diffusion recommenders, one of 14 directions in Diffusion models.
- Builds on 28 of its 49 references that are in the map.
- First posted on arXiv 23 Oct 2025.

## Placed in

- Diffusion models / [Diffusion recommenders](https://mlsanity.com/diffusion/diffusion-recommenders/)

## Builds on (most cited first, 28 of 28)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [DiffuRec: A Diffusion Model for Sequential Recommendation](https://mlsanity.com/paper/2304.00686/) (2023)
- [Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion](https://mlsanity.com/paper/2310.20453/) (2023)
- [Contrastive Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2304.03891/) (2023)
- [Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment](https://mlsanity.com/paper/2506.02221/) (2025)
- [Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations](https://mlsanity.com/paper/1910.02448/) (2019)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation](https://mlsanity.com/paper/2111.10093/) (2021)
- [A Survey on Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2401.04971/) (2024)
- [Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2407.11245/) (2024)
- [Pluralistic Off-policy Evaluation and Alignment](https://mlsanity.com/paper/2509.19333/) (2025)
- [Semi-Online Knowledge Distillation](https://mlsanity.com/paper/2111.11747/) (2021)
- [Dual Conditional Diffusion Models for Sequential Recommendation](https://mlsanity.com/paper/2410.21967/) (2024)
- [Contrastive Counterfactual Learning for Causality-aware Interpretable Recommender Systems](https://mlsanity.com/paper/2208.06746/) (2022)
- [Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2311.13188/) (2023)
- [ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2501.15118/) (2025)
- [Flow Matching for Collaborative Filtering](https://mlsanity.com/paper/2502.07303/) (2025)
- [Modeling Temporal Positive and Negative Excitation for Sequential Recommendation](https://mlsanity.com/paper/2410.22013/) (2024)
- [Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://mlsanity.com/paper/2410.22790/) (2024)
- [Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation](https://mlsanity.com/paper/2501.11671/) (2025)
- [FlowRec: Prior-Informed Flow Matching for Efficient Sequential Recommendation Generation](https://mlsanity.com/paper/2508.17618/) (2025)
- [Improved Knowledge Distillation via Adversarial Collaboration](https://mlsanity.com/paper/2111.14356/) (2021)
- [Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach](https://mlsanity.com/paper/2505.19544/) (2025)
- [Revisiting Self-attention for Cross-domain Sequential Recommendation](https://mlsanity.com/paper/2505.21811/) (2025)

## Built on by (most cited first, 1 of 1)

- [Escaping the Euclidean Void: Manifold-Informed Flow Matching for Sequential Recommendation](https://mlsanity.com/paper/2607.23762/) (2026)

## Abstract (arXiv)

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 textitGMFlowRec, 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.