Building Higher-Order Abstractions from the Components of Recommender Systems


  • Serdar Kadıoğlu Brown University Fidelity Investments
  • Bernard Kleynhans Fidelity Investments



Recommendation Systems , Machine Learning , Multidisciplinary Topics and Applications , Track: Deployed Innovative Tools


We present a modular recommender system framework that tightly integrates yet maintains the independence of individual components, thus satisfying two of the most critical aspects of industrial applications, generality and specificity. On the one hand, we ensure that each component remains self-contained and is ready to serve in other applications beyond recommender systems. On the other hand, when these components are combined, a unified theme emerges for recommender systems. We present the details of each component in the context of recommender systems and other applications. We release each component as an open-source library, and most importantly, we release their integration under MAB2REC, an industry-strength open-source software for building bandit-based recommender systems. By bringing standalone components together, Mab2Rec realizes a powerful and scalable toolchain to build and deploy business-relevant personalization applications. Finally, we share our experience and best practices for user training, adoption, performance evaluation, deployment, and model governance within the enterprise and the broader community.



How to Cite

Kadıoğlu, S., & Kleynhans, B. (2024). Building Higher-Order Abstractions from the Components of Recommender Systems. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 22998-23004.



IAAI Technical Track on Deployed Innovative Tools for Enabling AI Applications