FairRecKit is a web-based analysis software that supports researchers in performing, analyzing, and understanding recommendation computations. The idea behind FairRecKit is to facilitate the in-depth analysis of recommendation outcomes considering fairness aspects. With (nested) filters on user or item attributes, metrics can easily be compared across user and item subgroups. Further, (nested) filters can also be used on dataset level; this way, recommendation outcomes can be compared across several sub-datasets to analyze for differences considering fairness aspects.

This analysis software consists of two components: A software package (FairRecKitLib) that is used to run recommendation algorithms on the available datasets and a web-based user interface (FairRecKitApp) to start experiments, retrieve results of previous experiments, and analyze details.

FairRecKit has been developed by students from the Bachelor program Computer Science at Utrecht University within the Software Project course, commissioned by Christine Bauer.

Lennard Chung, Aleksej Cornelissen, Isabelle van Driessel, Diede van der Hoorn, Yme de Jong, Lan Le, Sanaz Najiyan Tabriz, Roderick Spaans, Casper Thijsen, Robert Verbeeten, Vos Wesseling, Fern Wieland

© Copyright Utrecht University (Department of Information and Computing Sciences)

If you use FairRecKit in published research, please cite:

Christine Bauer, Lennard Chung, Aleksej Cornelissen, Isabelle van Driessel, Diede van der Hoorn, Yme de Jong, Lan Le, Sanaz Najiyan Tabriz, Roderick Spaans, Casper Thijsen, Robert Verbeeten, Vos Wesseling, & Fern Wieland (2023). FairRecKit: A Web-based analysis software for recommender evaluations. Proceedings of the 8th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2023), Austin, TX, USA, 19–23 March, pp 438-443. DOI: 10.1145/3576840.3578274