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

TL;DR

WarpRec is a versatile, high-performance framework that unifies academic research and industrial deployment of recommender systems, emphasizing sustainability, reproducibility, and future integration with Agentic AI.

Contribution

It introduces a backend-agnostic architecture with extensive algorithms and metrics, enabling seamless transition from local to distributed training while promoting ecological responsibility.

Findings

Supports over 50 algorithms and 40 metrics.

Integrates real-time energy tracking with CodeCarbon.

Facilitates scalable, reproducible recommender system experiments.

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/