WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation
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arXiv
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| Format: | Preprint |
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2026
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| author | Avolio, Marco Aghilar, Potito Roccotelli, Sabino Anelli, Vito Walter Mallamaci, Chiara Paparella, Vincenzo Valentini, Marco Bellogín, Alejandro Trizio, Michelantonio Trotta, Joseph Ferrara, Antonio Di Noia, Tommaso |
| author_facet | Avolio, Marco Aghilar, Potito Roccotelli, Sabino Anelli, Vito Walter Mallamaci, Chiara Paparella, Vincenzo Valentini, Marco Bellogín, Alejandro Trizio, Michelantonio Trotta, Joseph Ferrara, Antonio Di Noia, Tommaso |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_17442 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation Avolio, Marco Aghilar, Potito Roccotelli, Sabino Anelli, Vito Walter Mallamaci, Chiara Paparella, Vincenzo Valentini, Marco Bellogín, Alejandro Trizio, Michelantonio Trotta, Joseph Ferrara, Antonio Di Noia, Tommaso Artificial Intelligence Information Retrieval 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/ |
| title | WarpRec: Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2602.17442 |