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

2026-02-19

AI Summary

This work addresses the significant gap between academic research and industrial deployment in recommender systems, where existing approaches often fail to balance methodological rigor with scalability. To bridge this divide, we propose WarpRec, a backend-agnostic framework that unifies local and distributed training paradigms. WarpRec integrates over 50 algorithms, 40 evaluation metrics, and 19 data splitting strategies, while incorporating CodeCarbon for energy consumption tracking and promoting ecological accountability. The framework enables efficient, reproducible, and energy-conscious development workflows, and is designed with forward compatibility to support the integration of Agentic AI and generative AI. By providing a unified architectural foundation, WarpRec facilitates the creation of sustainable, agent-ready next-generation recommender systems.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Multiagent Systems: Adversarial Agents
• Machine Learning: Hardware-aware ML

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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/

Problem

• Recommender Systems
• Academia-Industry Gap
• Scalability
• Reproducibility
• Sustainable AI

Innovation

• backend-agnostic architecture
• responsible AI
• reproducible recommendation
• scalable recommender systems
• Agentic AI