WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation

• URL: http://arxiv.org/abs/2602.17442v1
• Date: Thu, 19 Feb 2026 15:09:04 GMT
• 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,
• Abstract summary: We present WarpRec, a high-performance framework for Recommender Systems.<n>It includes state-of-the-art algorithms, 40 metrics, and 19 filtering and splitting strategies.<n>The framework enforces ecological responsibility by integrating CodeCarbon for real-time energy tracking.
• Score: 38.17743551493722
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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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