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

TL;DR

This paper introduces GMFlowRec, a novel generative framework using Gaussian Mixture Flow Matching for multi-domain sequential recommendation, effectively modeling complex domain transitions and outperforming existing methods.

Contribution

The paper presents GMFlowRec, a scalable and efficient model that captures diverse user behaviors across multiple domains using Gaussian Mixture Flow Matching and domain-aware transformers.

Findings

Achieves up to 44% improvement in NDCG@5 on JD and Amazon datasets.

Effectively models both dense and sparse domain transitions.

Maintains high efficiency with a unified backbone.

Abstract

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.