用流匹配提升推荐系统精度与速度，更好处理用户行为稀疏性。
Flow Matching for Collaborative Filtering
- 基于流匹配构建推荐模型，通过行为引导先验增强用户偏好建模
- 在多个数据集上达到顶尖推荐准确率，推理速度最快
- 适合追求高精度与低延迟的工业级推荐系统应用
生成模型在协同过滤中展现出巨大潜力，能捕捉用户兴趣与偏好的底层分布。然而，现有方法在后验近似不准确及与推荐数据离散特性不匹配方面存在局限，影响表达能力与实际性能。为此，我们提出 FlowCF，一种基于流匹配的新型推荐系统。通过两项关键创新：(1) 行为引导先验，对齐用户行为模式以应对稀疏且异构的用户-物品交互；(2) 离散流框架，保持隐式反馈的二值性，同时保留流匹配的稳定训练与高效推理优势。大量实验表明，FlowCF 在多个数据集上实现最先进推荐精度，并具备最快的推理速度，是实际推荐系统的有力候选。代码已开源：https://github.com/chengkai-liu/FlowCF。
原文摘要 · Abstract (English)
Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences. However, existing approaches struggle with inaccurate posterior approximations and misalignment with the discrete nature of recommendation data, limiting their expressiveness and real-world performance. To address these limitations, we propose FlowCF, a novel flow-based recommendation system leveraging flow matching for collaborative filtering. We tailor flow matching to the unique challenges in recommendation through two key innovations: (1) a behavior-guided prior that aligns with user behavior patterns to handle the sparse and heterogeneous user-item interactions, and (2) a discrete flow framework to preserve the binary nature of implicit feedback while maintaining the benefits of flow matching, such as stable training and efficient inference. Extensive experiments demonstrate that FlowCF achieves state-of-the-art recommendation accuracy across various datasets with the fastest inference speed, making it a compelling approach for real-world recommender systems. The code is available at https://github.com/chengkai-liu/FlowCF.
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