WarpRec让推荐系统研究兼具学术严谨与工业级效率，支持从本地到分布式无缝切换。
WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation
- 设计后端无关架构，支持50+算法与40+指标在本地与分布式间自由切换。
- 集成CodeCarbon实时追踪能耗，证明大规模训练可兼顾可持续性。
- 面向生成式AI时代，为推荐系统向智能体化演进提供可扩展架构基础。
推荐系统创新当前受限于研究与工业生态的割裂：研究者要么在内存中简单实验，要么需复杂重写才能部署到分布式工业引擎。为此，我们提出WarpRec，一种高性能框架，通过新颖的后端无关架构消除这一权衡。该框架包含50多个前沿算法、40个评估指标和19种过滤与划分策略，能无缝实现从本地执行到分布式训练与优化的过渡。框架通过集成CodeCarbon实现能耗实时追踪，表明规模化无需牺牲科学完整性与可持续性。此外，WarpRec预判了向智能体（Agentic AI）的演进趋势，推动推荐系统从静态排序引擎转变为生成式AI生态中的交互工具。综上，WarpRec不仅弥合了学术与工业的鸿沟，还可作为下一代可持续、智能体就绪推荐系统的架构基石。代码已开源：https://github.com/sisinflab/warprec/
原文摘要 · Abstract (English)
Innovation in Recommender Systems is currently impeded by a fractured ecosystem, where researchers must choose between the ease of in-memory experimentation and the costly, complex rewriting required for distributed industrial engines. To bridge this gap, we present WarpRec, a high-performance framework that eliminates this trade-off through a novel, backend-agnostic architecture. It includes 50+ state-of-the-art algorithms, 40 metrics, and 19 filtering and splitting strategies that seamlessly transition from local execution to distributed training and optimization. The framework enforces ecological responsibility by integrating CodeCarbon for real-time energy tracking, showing that scalability need not come at the cost of scientific integrity or sustainability. Furthermore, WarpRec anticipates the shift toward Agentic AI, leading Recommender Systems to evolve from static ranking engines into interactive tools within the Generative AI ecosystem. In summary, WarpRec not only bridges the gap between academia and industry but also can serve as the architectural backbone for the next generation of sustainable, agent-ready Recommender Systems. Code is available at https://github.com/sisinflab/warprec/
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