RecO

RecO is a cross-domain software framework designed to support the development and deployment of recommendation and optimization solutions. Standing for Recommendation and Optimization, RecO provides a modular platform that unifies data ingestion, model management, and optimization under a single workflow. The project emphasizes interoperability, enabling teams to combine machine learning models with optimization techniques to drive personalized experiences and efficient resource use.

RecO's architecture is layered. The data layer offers connectors to relational and non-relational stores and streaming systems, plus a feature store for reusable attributes. The model layer hosts a repository of reusable algorithms for ranking, prediction, and forecasting. The optimization layer provides constraint-based and heuristic solvers for decisions such as product recommendations, pricing, inventory, and routing. A serving layer exposes APIs for real-time inference and batch processing, while a policy and experiment layer supports A/B testing and explainability.

Historically, RecO emerged from collaborations between academic researchers and industry practitioners to address the gap between predictive modeling and prescriptive decision-making. It has been adopted by e-commerce platforms, logistics providers, and media publishers, and is maintained by a community-driven governance model. The project is released under an open-source license and encourages contributions from developers, data scientists, and operators.

RecO is used in applications such as personalized product recommendations, dynamic pricing, demand forecasting, inventory optimization, and route planning. It integrates with common data stacks and ML frameworks, supports scalable deployment on cloud or on-premises infrastructure, and emphasizes transparency and auditability of decisions.