@inproceedings{bauer_fairreckit_2023,
	address = {New York, NY, USA},
	title = {{FairRecKit}: {A} {Web}-based {Analysis} {Software} for {Recommender} {Evaluations}},
	url = {https://doi.org/10.1145/3576840.3578274},
	doi = {10.1145/3576840.3578274},
	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.},
	urldate = {2023-03-28},
	booktitle = {{CHIIR} '23},
	publisher = {Association for Computing Machinery},
	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 Najiyan Tabriz, Sanaz and Spaans, Roderick and Thijsen, Casper and Verbeeten, Robert and Wesseling, Vos and Wieland, Fern},
	month = mar,
	year = {2023},
	note = {Journal Abbreviation: CHIIR '23},
	keywords = {evaluation, music, web-based, recommender systems, toolkit, analysis, movies, resource},
	pages = {438--443},
}

{"_id":"vsj4ux9ohjJ2acmZg","bibbaseid":"bauer-chung-cornelissen-vandriessel-vanderhoorn-dejong-le-najiyantabriz-etal-fairreckitawebbasedanalysissoftwareforrecommenderevaluations-2023","author_short":["Bauer, C.","Chung, L.","Cornelissen, A.","van Driessel, I.","van der Hoorn, D.","de Jong, Y.","Le, L.","Najiyan Tabriz, S.","Spaans, R.","Thijsen, C.","Verbeeten, R.","Wesseling, V.","Wieland, F."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","address":"New York, NY, USA","title":"FairRecKit: A Web-based Analysis Software for Recommender Evaluations","url":"https://doi.org/10.1145/3576840.3578274","doi":"10.1145/3576840.3578274","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.","urldate":"2023-03-28","booktitle":"CHIIR '23","publisher":"Association for Computing Machinery","author":[{"propositions":[],"lastnames":["Bauer"],"firstnames":["Christine"],"suffixes":[]},{"propositions":[],"lastnames":["Chung"],"firstnames":["Lennard"],"suffixes":[]},{"propositions":[],"lastnames":["Cornelissen"],"firstnames":["Aleksej"],"suffixes":[]},{"propositions":["van"],"lastnames":["Driessel"],"firstnames":["Isabelle"],"suffixes":[]},{"propositions":["van","der"],"lastnames":["Hoorn"],"firstnames":["Diede"],"suffixes":[]},{"propositions":["de"],"lastnames":["Jong"],"firstnames":["Yme"],"suffixes":[]},{"propositions":[],"lastnames":["Le"],"firstnames":["Lan"],"suffixes":[]},{"propositions":[],"lastnames":["Najiyan","Tabriz"],"firstnames":["Sanaz"],"suffixes":[]},{"propositions":[],"lastnames":["Spaans"],"firstnames":["Roderick"],"suffixes":[]},{"propositions":[],"lastnames":["Thijsen"],"firstnames":["Casper"],"suffixes":[]},{"propositions":[],"lastnames":["Verbeeten"],"firstnames":["Robert"],"suffixes":[]},{"propositions":[],"lastnames":["Wesseling"],"firstnames":["Vos"],"suffixes":[]},{"propositions":[],"lastnames":["Wieland"],"firstnames":["Fern"],"suffixes":[]}],"month":"March","year":"2023","note":"Journal Abbreviation: CHIIR '23","keywords":"evaluation, music, web-based, recommender systems, toolkit, analysis, movies, resource","pages":"438–443","bibtex":"@inproceedings{bauer_fairreckit_2023,\n\taddress = {New York, NY, USA},\n\ttitle = {{FairRecKit}: {A} {Web}-based {Analysis} {Software} for {Recommender} {Evaluations}},\n\turl = {https://doi.org/10.1145/3576840.3578274},\n\tdoi = {10.1145/3576840.3578274},\n\tabstract = {FairRecKit is a web-based analysis software that supports researchers in\nperforming, analyzing, and understanding recommendation computations. The\nidea behind FairRecKit is to facilitate the in-depth analysis of\nrecommendation outcomes considering fairness aspects. With (nested)\nfilters on user or item attributes, metrics can easily be compared across\nuser and item subgroups. Further, (nested) filters can be used on the\ndataset level; this way, recommendation outcomes can be compared across\nseveral sub-datasets to analyze for differences considering fairness\naspects. The software currently features five datasets, 11 metrics, and 21\nrecommendation algorithms to be used in computational experimentation. It\nis open source and developed in a modular manner to facilitate extension.\nThe analysis software consists of two components: A software package\n(FairRecKitLib) for running recommendation algorithms on the available\ndatasets and a web-based user interface (FairRecKitApp) to start\nexperiments, retrieve results of previous experiments, and analyze\ndetails. The application also comes with extensive documentation and\noptions for result customization, which makes for a flexible tool that\nsupports in-depth analysis.},\n\turldate = {2023-03-28},\n\tbooktitle = {{CHIIR} '23},\n\tpublisher = {Association for Computing Machinery},\n\tauthor = {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 Najiyan Tabriz, Sanaz and Spaans, Roderick and Thijsen, Casper and Verbeeten, Robert and Wesseling, Vos and Wieland, Fern},\n\tmonth = mar,\n\tyear = {2023},\n\tnote = {Journal Abbreviation: CHIIR '23},\n\tkeywords = {evaluation, music, web-based, recommender systems, toolkit, analysis, movies, resource},\n\tpages = {438--443},\n}\n\n","author_short":["Bauer, C.","Chung, L.","Cornelissen, A.","van Driessel, I.","van der Hoorn, D.","de Jong, Y.","Le, L.","Najiyan Tabriz, S.","Spaans, R.","Thijsen, C.","Verbeeten, R.","Wesseling, V.","Wieland, F."],"key":"bauer_fairreckit_2023","id":"bauer_fairreckit_2023","bibbaseid":"bauer-chung-cornelissen-vandriessel-vanderhoorn-dejong-le-najiyantabriz-etal-fairreckitawebbasedanalysissoftwareforrecommenderevaluations-2023","role":"author","urls":{"Paper":"https://doi.org/10.1145/3576840.3578274"},"keyword":["evaluation","music","web-based","recommender systems","toolkit","analysis","movies","resource"],"metadata":{"authorlinks":{}},"downloads":6},"bibtype":"inproceedings","biburl":"https://api.zotero.org/users/6655/collections/3TB3KT36/items?key=VFvZhZXIoHNBbzoLZ1IM2zgf&format=bibtex&limit=100","dataSources":["HB6fr7bPytW2CAAzC","7KNAjxiv2tsagmbgY"],"keywords":["evaluation","music","web-based","recommender systems","toolkit","analysis","movies","resource"],"search_terms":["fairreckit","web","based","analysis","software","recommender","evaluations","bauer","chung","cornelissen","van driessel","van der hoorn","de jong","le","najiyan tabriz","spaans","thijsen","verbeeten","wesseling","wieland"],"title":"FairRecKit: A Web-based Analysis Software for Recommender Evaluations","year":2023,"downloads":6}