用流匹配改进推荐模型，更准更稳地捕捉用户偏好
Flow Matching based Sequential Recommender Model
- 采用直线流轨迹和定制损失函数，优化推荐过程
- 在4个数据集上平均提升6.53%，优于当前最优方法
- 适合需要高精度、低噪声推荐的工业场景
生成模型，尤其是扩散模型，已成为序列推荐的强大工具。然而，由于扩散方法中前向和反向过程固有的噪声扰动，准确建模用户偏好仍具挑战性。为此，本文提出FMRec，一种基于流匹配的推荐模型，采用直线流轨迹和针对推荐任务优化的损失函数。从扩散模型视角出发，我们引入重建损失以增强对噪声扰动的鲁棒性，从而在前向过程中保留用户偏好。在反向过程中，采用确定性反向采样器（基于ODE的更新函数），消除不必要的随机性，确保生成推荐与用户需求高度一致。在四个基准数据集上的广泛实验表明，FMRec相较于当前最优方法平均提升6.53%。复现代码已公开于 https://github.com/FengLiu-1/FMRec。
原文摘要 · Abstract (English)
Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due to the noise perturbations inherent in the forward and reverse processes of diffusion-based methods. Towards this end, this study introduces FMRec, a Flow Matching based model that employs a straight flow trajectory and a modified loss tailored for the recommendation task. Additionally, from the diffusion-model perspective, we integrate a reconstruction loss to improve robustness against noise perturbations, thereby retaining user preferences during the forward process. In the reverse process, we employ a deterministic reverse sampler, specifically an ODE-based updating function, to eliminate unnecessary randomness, thereby ensuring that the generated recommendations closely align with user needs. Extensive evaluations on four benchmark datasets reveal that FMRec achieves an average improvement of 6.53% over state-of-the-art methods. The replication code is available at https://github.com/FengLiu-1/FMRec.
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