![[2602.17442] WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.17442] WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation
Summary
WarpRec presents a high-performance framework for recommender systems, merging academic rigor with industrial scalability, while promoting ecological responsibility.
Why It Matters
This framework addresses the current fragmentation in recommender systems research, enabling seamless transitions from local to distributed environments. By integrating energy tracking, WarpRec emphasizes sustainability alongside performance, making it relevant for both researchers and industry practitioners aiming for responsible AI development.
Key Takeaways
- WarpRec bridges the gap between academic research and industrial application in recommender systems.
- It features over 50 algorithms and metrics, facilitating efficient experimentation and deployment.
- The framework incorporates real-time energy tracking for sustainable AI practices.
- WarpRec prepares recommender systems for future developments in Agentic AI.
- It promotes reproducibility and efficiency in AI research and applications.
Computer Science > Artificial Intelligence arXiv:2602.17442 (cs) [Submitted on 19 Feb 2026] Title:WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation Authors:Marco Avolio, Potito Aghilar, Sabino Roccotelli, Vito Walter Anelli, Chiara Mallamaci, Vincenzo Paparella, Marco Valentini, Alejandro Bellogín, Michelantonio Trizio, Joseph Trotta, Antonio Ferrara, Tommaso Di Noia View a PDF of the paper titled WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation, by Marco Avolio and 11 other authors View PDF HTML (experimental) Abstract: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...
