Session: Mab2Rec: A library for building bandit-based recommenders
Building recommendation models and deploying recommender systems require non-trivial effort and investment. Training recommender algorithms require sophisticated tools and expertise in feature engineering, model selection, and evaluation, while deploying large-scale recommender systems require sophisticated engineering effort.
In this talk, we present Mab2Rec, an open-source library for building bandit-based recommender systems developed by the AI Center of Excellence at Fidelity Investments. Our modular system design provides powerful and scalable framework for building and deploying recommender applications, while also allowing individual components to be re-used beyond recommender systems. We outline each component in the context of recommender systems and release industrial-strength open-source software with the broader community.
This overview can serve as a starting point for software developers and data science practitioners in their efforts in building similar systems.
