State of the art recommendation algorithms are increasingly complex and no longer one-size fits all, and advances often only address specific scenarios. OpenRec, a project funded by the Connected Experiences Lab at Cornell Tech and the National Science Foundation (NSF), provides a modular architecture to easily adapt, extend, and compare algorithms for heterogeneous use cases. 

The framework, developed by Longqi Yang, a PhD candidate at Cornell Tech and Deborah Estrin, Associate Dean and Robert V. Tishman ’37 Professor, was developed for researchers to develop and evaluate algorithms across a range of use cases and practitioners to customize state-of-the-art solutions.