%0 Conference Paper %1 bauer2023_fairreckit %A Bauer, Christine %A Chung, Lennard %A Cornelissen, Aleksej %A van Driessel, Isabelle %A van der Hoorn, Diede %A de Jong, Yme %A Le, Lan %A Tabriz, Sanaz Najiyan %A Spaans, Roderick %A Thijsen, Casper %A Verbeeten, Robert %A Wesseling, Vos %A Wieland, Fern %B Proceedings of the 2023 Conference on Human Information Interaction and Retrieval %C New York, NY, USA %D 2023 %I ACM %K %P 438-443 %R 10.1145/3576840.3578274 %T FairRecKit: A Web-based Analysis Software for Recommender Evaluations %U https://doi.org/10.1145%2F3576840.3578274 %X 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 be used on the dataset level; this way, recommendation outcomes can be compared across several sub-datasets to analyze for differences considering fairness aspects. The software currently features five datasets, 11 metrics, and 21 recommendation algorithms to be used in computational experimentation. It is open source and developed in a modular manner to facilitate extension. The analysis software consists of two components: A software package (FairRecKitLib) for running recommendation algorithms on the available datasets and a web-based user interface (FairRecKitApp) to start experiments, retrieve results of previous experiments, and analyze details. The application also comes with extensive documentation and options for result customization, which makes for a flexible tool that supports in-depth analysis. %@ 9798400700354

@inproceedings{bauer2023_fairreckit, abstract = {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 be used on the dataset level; this way, recommendation outcomes can be compared across several sub-datasets to analyze for differences considering fairness aspects. The software currently features five datasets, 11 metrics, and 21 recommendation algorithms to be used in computational experimentation. It is open source and developed in a modular manner to facilitate extension. The analysis software consists of two components: A software package (FairRecKitLib) for running recommendation algorithms on the available datasets and a web-based user interface (FairRecKitApp) to start experiments, retrieve results of previous experiments, and analyze details. The application also comes with extensive documentation and options for result customization, which makes for a flexible tool that supports in-depth analysis.}, added-at = {2023-12-13T05:11:02.000+0100}, address = {New York, NY, USA}, author = {Bauer, Christine and Chung, Lennard and Cornelissen, Aleksej and van Driessel, Isabelle and van der Hoorn, Diede and de Jong, Yme and Le, Lan and Tabriz, Sanaz Najiyan and Spaans, Roderick and Thijsen, Casper and Verbeeten, Robert and Wesseling, Vos and Wieland, Fern}, biburl = {https://www.bibsonomy.org/bibtex/26043b85ff328672fe2a0b26980a0d312/admin}, booktitle = {Proceedings of the 2023 Conference on Human Information Interaction and Retrieval}, doi = {10.1145/3576840.3578274}, eventtitle = {8th ACM SIGIR Conference on Human Information Interaction and Retrieval}, interhash = {9f758d3a0654b75cd30d4c8e82458bd8}, intrahash = {6043b85ff328672fe2a0b26980a0d312}, isbn = {9798400700354}, keywords = {}, language = {en}, month = mar, pages = {438-443}, publisher = {ACM}, series = {CHIIR 2023}, timestamp = {2023-12-13T05:11:02.000+0100}, title = {FairRecKit: A Web-based Analysis Software for Recommender Evaluations}, url = {https://doi.org/10.1145%2F3576840.3578274}, venue = {Austin, TX, USA}, year = 2023 }