FairnessRecSys_Survey2023

A table of publications on fairness in recommender systems. This page will be periodically updated to include the most recent works. Please contact us if your work is not in the list.

The table serves as overview and extension of the works discussed in the following survey. Please consider citing it if you used the survey.

@article{deldjoo2023FairRecSys,
  title={Fairness in Recommender Systems: Research Landscape and Future Directions},
  author={Deldjoo, Yashar; Jannach, Dietmar; Bellogin, Alejandro; Difonzo, Alessandro; Zanzonelli, Dario},
  journal={User Modeling and User-Adapted Interaction (UMUAI)},
  year={2023},
  publisher={Springer}
}

Papers

Consumer Fairness

Year	Authors	Title	Venue	Dataset	Attribute	Code	Key points
2022	Bower et al.	Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems	FAccTRec	GermanCredit, Syntheic	Demographics		Shows how randomized ranking can increase inequality.
2021	Qiang Dong, Shuang-Shuang Xie, Wen-Jun Li	User-Item Matching for Recommendation Fairness.		[Movielens, Netflix]	, item popularity,
2021	Diego Corrêa da Silva, Marcelo Garcia Manzato, Frederico Araújo Durão	Exploiting personalized calibration and metrics for fairness recommendation.	Expert Syst. Appl.	[Movielens 20M, Yahoo Movies]	, item popularity,
2021	Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín, Tommaso Di Noia	A flexible framework for evaluating user and item fairness in recommender systems.	UMUAI	[Amazon Review dataset, Movielens 1M]	[happiness, helpfulness, interactions, age, gender], [price, year, popularity],
2021	Giandomenico Cornacchia, Fedelucio Narducci, Azzurra Ragone	A General Model for Fair and Explainable Recommendation in the Loan Domain (Short paper).	KaRS/ComplexRec@RecSys		Gender and others (according to laws), ,
2021	Víctor Corcoba Magaña, Xabiel G. Pañeda, Alejandro Garcia Tuero, Laura Pozueco, Roberto García, David Melendi, Abel Rionda	A Method for Making a Fair Evaluation of Driving Styles in Different Scenarios With Recommendations for Their Improvement.	IEEE Intell. Transp. Syst. Mag.		, ,
2021	Jakob Schöffer, Niklas Kuehl, Isabel Valera	A Ranking Approach to Fair Classification.	COMPASS		, ,
2021	Sinan Seymen, Himan Abdollahpouri, Edward C. Malthouse	A Unified Optimization Toolbox for Solving Popularity Bias, Fairness, and Diversity in Recommender Systems.	MORS@RecSys	MovieLens	, ,	https://github.com/sseymen-tech/unified_toolbox
2021	Kai Lukoff	Addressing Present Bias in Movie Recommender Systems and Beyond.	IUI Workshops		, ,
2021	Ludovico Boratto, Mirko Marras	Advances in Bias-aware Recommendation on the Web.	WSDM		, ,
2021	Tim Draws, Nava Tintarev, Ujwal Gadiraju	Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics.	SIGKDD Explor.		, ,
2021	Aleksandr Petrov, Yuriy Makarov	Attention-based neural re-ranking approach for next city in trip recommendations.	WebTour@WSDM		, ,
2021	Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, Keping Yang	AutoDebias: Learning to Debias for Recommendation.	SIGIR	[Yahoo!R3, Coat, Synthetic]	, item popularity,	https://github.com/DongHande/AutoDebias
2021	Mehdi Elahi, Himan Abdollahpouri, Masoud Mansoury, Helma Torkamaan	Beyond Algorithmic Fairness in Recommender Systems.	UMAP (Adjunct Publication)	N.A.	various individual differences, ,
2021	Michael Matthias Voit, Heiko Paulheim	Bias in Knowledge Graphs - An Empirical Study with Movie Recommendation and Different Language Editions of DBpedia.	LDK	[Movielens 1M]	, [genre, country],	https://github.com/voitijaner/Movie-RSs-Master-Thesis-Submission-Voit
2021	Joanna Misztal-Radecka, Bipin Indurkhya	Bias-Aware Hierarchical Clustering for detecting the discriminated groups of users in recommendation systems.	Inf. Process. Manag.	[Movielens 100K, Book-Crossing, synthetic]	not a precise attribute (automatically detected clusters), ,
2021	Ke Yang, Joshua R. Loftus, Julia Stoyanovich	Causal Intersectionality and Fair Ranking.	FORC	[COMPAS, synthetic]	[gender, race], ,	https://github.com/DataResponsibly/CIFRank
2021	Yang Zhang, Fuli Feng, Xiangnan He, Tianxin Wei, Chonggang Song, Guohui Ling, Yongdong Zhang	Causal Intervention for Leveraging Popularity Bias in Recommendation.	SIGIR	[Kwai, Douban, Tencent]	, Popularity (here we actually want that output is affected by the beneficial part of it, as opposed to the neutrality wrt sensitive attributes),	https://github.com/zyang1580/PDA
2021	Ruihong Qiu, Sen Wang, Zhi Chen, Hongzhi Yin, Zi Huang	CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation.	ACM Multimedia	[Amazon]	, visual feature (?),
2021	Bruna D. Wundervald	Cluster-based quotas for fairness improvements in music recommendation systems.	Int. J. Multim. Inf. Retr.	[LFM-1b]	, Popularity,	shorturl.at/iOS78
2021	Xiaojie Wang, Rui Zhang, Yu Sun, Jianzhong Qi	Combating Selection Biases in Recommender Systems with a Few Unbiased Ratings.	WSDM	[MUSIC,COAT]	, ,
2021	Julia Stoyanovich	Comparing Apples and Oranges: Fairness and Diversity in Ranking (Invited Talk).	ICDT		, ,
2021	Harrie Oosterhuis	Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness.	SIGIR	[yahoo,mslr,istella]	, ,	https://github.com/HarrieO/2021-SIGIR-plackett-luce
2021	Ludovico Boratto, Gianni Fenu, Mirko Marras	Connecting user and item perspectives in popularity debiasing for collaborative recommendation.	Inf. Process. Manag.	[COCO,ML1M]	, ,
2021	Shantanu Gupta, Hao Wang, Zachary Lipton, Yuyang Wang	Correcting Exposure Bias for Link Recommendation.	ICML	[MAG, synthetic]	, Item exposure,	https://github.com/shantanu95/exposure-bias-link-rec
2021	Deena Abul-Fottouh, Melodie Yun-Ju Song, Anatoliy A. Gruzd	Corrigendum to "Examining algorithmic biases in YouTube"s recommendations of vaccine videos" [Int. J. Med. Inf. 140 (2020) 104175].	Int. J. Medical Informatics		, ,
2021	Yusuke Narita, Shota Yasui, Kohei Yata	Debiased Off-Policy Evaluation for Recommendation Systems.	RecSys		, ,
2021	Rashidul Islam, Kamrun Naher Keya, Ziqian Zeng, Shimei Pan, James R. Foulds	Debiasing Career Recommendations with Neural Fair Collaborative Filtering.	WWW	[Movielens,Facebook dataset]	Gender, ,
2021	Jesús Bobadilla, Raúl Lara-Cabrera, Ángel González-Prieto, Fernando Ortega	DeepFair: Deep Learning for Improving Fairness in Recommender Systems.	Int. J. Interact. Multim. Artif. Intell.	MovieLens 1M	gender,age, ,
2021	Laura Schelenz	Diversity-aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender Systems.	UMAP (Adjunct Publication)		, ,
2021	Tom Sühr, Sophie Hilgard, Himabindu Lakkaraju	Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring.	AIES	TaskRabbit Queries, Survey on participants	age,gender,income,education, ,
2021	Ömer Kirnap, Fernando Diaz, Asia Biega, Michael D. Ekstrand, Ben Carterette, Emine Yilmaz	Estimation of Fair Ranking Metrics with Incomplete Judgments.	WWW	[TREC, GoodReads, synthetic]	, [gender],
2021	Yashar Deldjoo, Alejandro Bellogín, Tommaso Di Noia	Explaining recommender systems fairness and accuracy through the lens of data characteristics.	Inf. Process. Manag.	Movielens ML-1M, ml-100K, BookCrossing dataset	[gender], ,
2021	Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos	Fair multi-stakeholder news recommender system with hypergraph ranking.	Inf. Process. Manag.	[Roularta, Adressa]	, author popularity,
2021	Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, James Caverlee	Fairness among New Items in Cold Start Recommender Systems.	SIGIR	[ML1M, ML20M, CiteULike, XING]	, cold items,	https://github.com/Zziwei/Fairness-in-Cold-Start-Recommendation
2021	Nasim Sonboli, Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler	Fairness and Transparency in Recommendation: The Users" Perspective.	UMAP	interviews from 30 participants	, generic,
2021	Theodoros Giannakas, Pavlos Sermpezis, Anastasios Giovanidis, Thrasyvoulos Spyropoulos, George Arvanitakis	Fairness in Network-Friendly Recommendations.	WOWMOM	[LastFM, Movielens]	, generic,
2021	Evaggelia Pitoura, Kostas Stefanidis, Georgia Koutrika	Fairness in Rankings and Recommenders: Models, Methods and Research Directions.	ICDE		, ,
2021	Evaggelia Pitoura, Kostas Stefanidis, Georgia Koutrika	Fairness-aware Methods in Rankings and Recommenders.	MDM		, ,
2021	Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, Xing Xie	Fairness-aware News Recommendation with Decomposed Adversarial Learning.	AAAI	Taken from MSN News	gender, ,
2021	Masoud Mansoury	Fairness-Aware Recommendation in Multi-Sided Platforms.	WSDM		, ,
2021	Ladislav Malecek, Ladislav Peska	Fairness-preserving Group Recommendations With User Weighting.	UMAP (Adjunct Publication)	[Movielens 1M, KGRec]	generic, ,	https://github.com/LadislavMalecek/UMAP2021
2021	Akrati Saxena, George Fletcher, Mykola Pechenizkiy	How Fair is Fairness-aware Representative Ranking?	WWW (Companion Volume)	synthetic	activity (considering the whole universe), generic, ,
2021	Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin, Yuekai Sun	Individually Fair Rankings with SenSTIR ( Sensitive Set Transport Invariant Ranking)	ICLR	synthetic, german credit, Microsoft Learn to Rank	, ,	https://github.com/ashudeep/Fair-PGRank
2021	Giorgos Giannopoulos, George Papastefanatos, Dimitris Sacharidis, Kostas Stefanidis	Interactivity, Fairness and Explanations in Recommendations.	UMAP (Adjunct Publication)		, ,
2021	Ludovico Boratto, Gianni Fenu, Mirko Marras	Interplay between upsampling and regularization for provider fairness in recommender systems.	User Model. User Adapt. Interact.	Movielens 10M, COCO Course collection	, Gender of the director, gender of the instuctor,
2021	Emre Yalcin, Alper Bilge	Investigating and counteracting popularity bias in group recommendations.	Inf. Process. Manag.	[Movielens100k, movielens 1m. Ciao20	, ,
2021	Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, Markus Schedl	Investigating gender fairness of recommendation algorithms in the music domain.	Inf. Process. Manag.		age, other(Lookup name), ,
2021	Mehdi Elahi, Danial Khosh Kholgh, Mohammad Sina Kiarostami, Sorush Saghari, Shiva Parsa Rad, Marko Tkalcic	Investigating the impact of recommender systems on user-based and item-based popularity bias.	Inf. Process. Manag.		, ,
2021	Robin Vogel, Aurélien Bellet, Stéphan Clémençon	Learning Fair Scoring Functions: Bipartite Ranking under ROC-based Fairness Constraints.	AISTATS	[Compas, Adult]	[race, gender], ,
2021	Caitlin Kuhlman, Walter Gerych, Elke A. Rundensteiner	Measuring Group Advantage: A Comparative Study of Fair Ranking Metrics.	AIES	[NFL players, synthetic]	generic, ,	https://github.com/waltergerych/AIES_2021_Measuring_Group_Advantage
2021	Chen Lin, Xinyi Liu, Guipeng Xv, Hui Li	Mitigating Sentiment Bias for Recommender Systems.	SIGIR	[Amazon, Yelp]	sentiment polarity, sentiment polarity,
2021	Jing Yuan	Modeling and analyzing bias in recommender systems from multi-views: context, topic and evaluation.	Jing Yuan		, ,
2021	Guangli Li, Jianwu Zhuo, Chuanxiu Li, Jin Hua, Tian Yuan, Zhengyu Niu, Donghong Ji, Renzhong Wu, Hongbin Zhang	Multi-modal visual adversarial Bayesian personalized ranking model for recommendation.	Inf. Sci.		, ,
2021	Rodrigo Borges, Kostas Stefanidis	On mitigating popularity bias in recommendations via variational autoencoders.	SAC	[Movielens 20M]	, popularity,	https://github.com/rcaborges/popularity-bias-vae
2021	Sruthi Gorantla, Amit Deshpande, Anand Louis	On the Problem of Underranking in Group-Fair Ranking.	ICML	[Compas, German Credit Risk]	[age, race, gender], ,	https://github.com/sruthigorantla/FIGR
2021	Ananya Gupta, Eric Johnson, Justin Payan, Aditya Kumar Roy, Ari Kobren, Swetasudha Panda, Jean-Baptiste Tristan, Michael L. Wick	Online Post-Processing in Rankings for Fair Utility Maximization.	WSDM	[synthetic, German Credit, AirBnB, Stack Exchange, Resume]	[gender, age, reputation, region of residence], [price range],	https://github.com/ejohnson0430/fair_online_ranking
2021	Qianxiu Hao, Qianqian Xu, Zhiyong Yang, Qingming Huang	Pareto Optimality for Fairness-constrained Collaborative Filtering.	ACM Multimedia	synthetic,Netflix	interaction, ,
2021	Nyi Nyi Htun, Elisa Lecluse, Katrien Verbert	Perception of Fairness in Group Music Recommender Systems.	IUI	generated with spotify api	Personaliy, ,
2021	Himank Yadav, Zhengxiao Du, Thorsten Joachims	Policy-Gradient Training of Fair and Unbiased Ranking Functions.	SIGIR	microsoft learning to rank, german credit	, gender, price, brand, etc..,	https://github.com/him229/fultr
2021	Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel	Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems.	WSDM	sinthetic data,german credit	gender, ,
2021	Robin Burke, Michael D. Ekstrand, Nava Tintarev, Julita Vassileva	Preface to the special issue on fair, accountable, and transparent recommender systems.	User Model. User Adapt. Interact.		, ,
2021	Saman Forouzandeh, Mehrdad Rostami, Kamal Berahmand	Presentation a Trust Walker for rating prediction in recommender system with Biased Random Walk: Effects of H-index centrality, similarity in items and friends.	Eng. Appl. Artif. Intell.		, ,
2021	Weizhi Ying, Qing Yu, Zuohua Wang	Social Recommendation Combining Implicit Information and Rating Bias.	CSCWD		, ,
2021	Jianli Zhao, Shangcheng Yang, Huan Huo, Qiuxia Sun, Xijiao Geng	TBTF: an effective time-varying bias tensor factorization algorithm for recommender system.	Appl. Intell.		, ,
2021	Yao Wu, Jian Cao, Guandong Xu, Yudong Tan	TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers.	SIGIR	[Ctrip, Google Local, Amazon Review]	, item provider,	https://zenodo.org/record/4527725#.YbMhuJGZNPZ
2021	Elizabeth Gómez, Carlos Shui Zhang, Ludovico Boratto, Maria Salamó, Mirko Marras	The Winner Takes it All: Geographic Imbalance and Provider (Un)fairness in Educational Recommender Systems.	SIGIR	COCO	, geographic location of teachers(Items = courses),
2021	Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon, Benjamin Timmermans	This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics.	SIGIR	18 different topics from ProCon	, , likert scale rating survey
2021	Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, Yongfeng Zhang	Towards Long-term Fairness in Recommendation.	WSDM	[Movielens100K, Movielens1M]	, popularity,	https://github.com/TobyGE/FCPO
2021	Yunqi Li, Yingqiang Ge, Yongfeng Zhang	Tutorial on Fairness of Machine Learning in Recommender Systems.	SIGIR		, ,
2021	Aadi Swadipto Mondal, Rakesh Bal, Sayan Sinha, Gourab K. Patro	Two-Sided Fairness in Non-Personalised Recommendations (Student Abstract).	AAAI		, ,
2021	Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, Edward C. Malthouse	User-centered Evaluation of Popularity Bias in Recommender Systems.	UMAP	[LFM-1b, Movielens1M]	mainstreaminess (i.e., level of interest in popular items), ,
2021	Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, Yongfeng Zhang	User-oriented Fairness in Recommendation.	WWW	Amazon	activity level (# of interactions, total consumption, max price), ,	https://github.com/rutgerswiselab/user-fairness
2021	Avijit Ghosh, Ritam Dutt, Christo Wilson	When Fair Ranking Meets Uncertain Inference.	SIGIR	[Chess, Crunchbase entrepreneurs, Equestrian]	gender, race, ,	https://github.com/evijit/SIGIR_FairRanking_UncertainInference
2021	Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Krishna P. Gummadi	When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces.	FAccT	crawled (Amazon item-to-item recs)	, brand,
2020	Fátima Leal, Bruno Veloso, Benedita Malheiro, Horacio González-Vélez, Juan-Carlos Burguillo	A 2020 perspective on "Scalable modelling and recommendation using wiki-based crowdsourced repositories: " Fairness, scalability, and real-time recommendation.	Electron. Commer. Res. Appl.		, ,
2020	Jiarui Jin, Yuchen Fang, Weinan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai	A Deep Recurrent Survival Model for Unbiased Ranking.	SIGIR
2020	Guilherme Ramos, Carlos Caleiro	A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity.	BIAS
2020	Mingming Li, Fuqing Zhu, Jiao Dai, Liangjun Zang, Yipeng Su, Jizhong Han, Songlin Hu	A Rating Bias Formulation based on Fuzzy Set for Recommendation.	IJCNN
2020	Mengting Wan, Jianmo Ni, Rishabh Misra, Julian J. McAuley	Addressing Marketing Bias in Product Recommendations.	WSDM	e-commerce	Body shape, Gender	https://github.com/MengtingWan/marketBias
2020	Yang Xiao, Qingqi Pei, Lina Yao, Shui Yu, Lei Bai, Xianzhi Wang	An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness.	J. Netw. Comput. Appl.	Epinions, Douban, Ciao	activeness (within the group)
2020	Pablo Sánchez, Alejandro Bellogín	Applying reranking strategies to route recommendation using sequence-aware evaluation.	User Model. User Adapt. Interact.
2020	Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, Cong Geng	Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison.	RecSys			GitHub - AmazingDD/daisyRec: A developing recommender system in pytorch. Algorithm: KNN, LFM, SLIM, NeuMF, FM, DeepFM, VAE and so on, which aims to fair comparison for recommender system benchmarks
2020	Weiwen Liu, Feng Liu, Ruiming Tang, Ben Liao, Guangyong Chen, Pheng-Ann Heng	Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning.	PAKDD (1)	Movielens100K, Kiva	geographical region
2020	Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo	Bias and Social Aspects in Search and Recommendation - First International Workshop, BIAS 2020, Lisbon, Portugal, April 14, 2020, Proceedings.
2020	Ricardo Baeza-Yates	Bias in Search and Recommender Systems.	RecSys
2020	Gunay Kazimzade, Milagros Miceli	Biased Priorities, Biased Outcomes: Three Recommendations for Ethics-oriented Data Annotation Practices.	AIES
2020	Dimitris Sacharidis	Building User Trust in Recommendations via Fairness and Explanations.	UMAP (Adjunct Publication)
2020	Ruoyuan Gao, Chirag Shah	Counteracting Bias and Increasing Fairness in Search and Recommender Systems.	RecSys
2020	Malte-Levin Jauer, Thomas M. Deserno	Data Provenance Standards and Recommendations for FAIR Data.	MIE
2020	Diego Carraro, Derek Bridge	Debiased Offline Evaluation of Active Learning in Recommender Systems.	FLAIRS Conference
2020	Diego Carraro, Derek Bridge	Debiased offline evaluation of recommender systems: a weighted-sampling approach.	SAC
2020	Lele Cao, Sahar Asadi, Matteo Biasielli, Michael Sjöberg	Debiasing Few-Shot Recommendation in Mobile Games.	ORSUM@RecSys
2020	Tobias Schnabel, Paul N. Bennett	Debiasing Item-to-Item Recommendations With Small Annotated Datasets.	RecSys
2020	Tingting Zhao, Guo Sun, Xia Feng, Liangmin Wang	Design of Educational Resources-oriented Fair Recommendation System Based on Consortium Blockchain.	NaNA
2020	Mesut Kaya, Derek G. Bridge, Nava Tintarev	Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance.	RecSys	[Movielens1M, KGRec]		https://github.com/mesutkaya/recsys2020
2020	Deena Abul-Fottouh, Melodie Yun-Ju Song, Anatoliy A. Gruzd	Examining algorithmic biases in YouTube"s recommendations of vaccine videos.	Int. J. Medical Informatics
2020	Diego Sánchez-Moreno, Vivian F. López Batista, María Dolores Muñoz Vicente, Ángel Luis Sánchez Lázaro, María N. Moreno García	Exploiting the User Social Context to Address Neighborhood Bias in Collaborative Filtering Music Recommender Systems.	Inf.	Hetrec2011-lastfm	popularity
2020	Dougal Shakespeare, Lorenzo Porcaro, Emilia Gómez, Carlos Castillo	Exploring Artist Gender Bias in Music Recommendation.	ComplexRec-ImpactRS@RecSys	LastFM-360K, LastFM-1b	artist gender	https://github.com/dshakes90/Last-fm-Gender-Bias-Analysis
2020	Maria Stratigi, Jyrki Nummenmaa, Evaggelia Pitoura, Kostas Stefanidis	Fair sequential group recommendations.	SAC	Movielens20M
2020	Guang Wang, Yongfeng Zhang, Zhihan Fang, Shuai Wang, Fan Zhang, Desheng Zhang	FairCharge: A Data-Driven Fairness-Aware Charging Recommendation System for Large-Scale Electric Taxi Fleets.	Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.	self produced
2020	Bora Edizel, Francesco Bonchi, Sara Hajian, André Panisson, Tamir Tassa	FaiRecSys: mitigating algorithmic bias in recommender systems.	Int. J. Data Sci. Anal.	Movielens, Reddit:Movielens	Gender	https://github.com/zuohuif/FairKG4Rec
2020	Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke	FairMatch: A Graph-based Approach for Improving Aggregate Diversity in Recommender Systems.	UMAP	epinions,movielens
2020	Alarith Uhde, Nadine Schlicker, Dieter P. Wallach, Marc Hassenzahl	Fairness and Decision-making in Collaborative Shift Scheduling Systems.	CHI
2020	Dimitris Sacharidis, Carine Pierrette Mukamakuza, Hannes Werthner	Fairness and Diversity in Social-Based Recommender Systems.	UMAP (Adjunct Publication)	Douban,Epinions	Interactions of friends
2020	Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, Gerard de Melo	Fairness-Aware Explainable Recommendation over Knowledge Graphs.	SIGIR	amazon item e-commerce dataset	Interactions	FairKG4Rec
2020	Nasim Sonboli, Robin Burke, Zijun Liu, Masoud Mansoury	Fairness-aware Recommendation with librec-auto.	RecSys
2020	Gourab K. Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, Abhijnan Chakraborty	FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms.	WWW	Google Local, LastFM	Both	gourabkumarpatro/FairRec_www_2020: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms (github.com)
2020	Qiliang Zhu, Qibo Sun, Zengxiang Li, Shangguang Wang	FARM: A Fairness-Aware Recommendation Method for High Visibility and Low Visibility Mobile APPs.	IEEE Access	crawled	id, app reputation, number of user records, average rating
2020	Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke	Feedback Loop and Bias Amplification in Recommender Systems.	CIKM
2020	Ludovico Boratto, Mirko Marras	Hands on Data and Algorithmic Bias in Recommender Systems.	UMAP
2020	Ramazan Esmeli, Mohamed Bader-El-Den, Hassana Abdullahi, David Henderson	Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking.	IJCNN
2020	Ludovico Boratto, Mirko Marras, Stefano Faralli, Giovanni Stilo	International Workshop on Algorithmic Bias in Search and Recommendation (Bias 2020).	ECIR (2)
2020	Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof	Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems.	RecSys
2020	Ziwei Zhu, Jianling Wang, James Caverlee	Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems.	SIGIR
2020	Alessandro B. Melchiorre, Eva Zangerle, Markus Schedl	Personality Bias of Music Recommendation Algorithms.	RecSys
2020	Harrie Oosterhuis, Maarten de Rijke	Policy-Aware Unbiased Learning to Rank for Top-k Rankings.	SIGIR
2020	Orestis Papakyriakopoulos, Juan Carlos Medina Serrano, Simon Hegelich	Political communication on social media: A tale of hyperactive users and bias in recommender systems.	Online Soc. Networks Media
2020	Wesley Silva, Marcos Spalenza, Jean-Rémi Bourguet, Elias de Oliveira	Recommendation Filtering à la carte for Intelligent Tutoring Systems.	BIAS
2020	Hylke Koers, Daniel Bangert, Emilie Hermans, René van Horik, Maaike de Jong, Mustapha Mokrane	Recommendations for Services in a FAIR Data Ecosystem.	Patterns
2020	Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo	Report on the international workshop on algorithmic bias in search and recommendation (Bias 2020).	SIGIR Forum
2020	Harrie Oosterhuis, Maarten de Rijke	Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking.	ICTIR
2020	Toyin Clottey, W. C. Benton Jr.	Technical Note: Recommendations for Assessing Unit Nonresponse Bias in Dyadic Focused Empirical Supply Chain Management Research.	Decis. Sci.
2020	Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher	The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation.	RecSys	Movielens, yahoo	interest in popular items
2020	Dominik Kowald, Markus Schedl, Elisabeth Lex	The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study.	ECIR (2)
2020	Alisa Rieger, Mariët Theune, Nava Tintarev	Toward Natural Language Mitigation Strategies for Cognitive Biases in Recommender Systems.	NL4XAI@INGL
2020	Shuqi Xu, Manuel Sebastian Mariani, Linyuan Lü, Matús Medo	Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data.	J. Informetrics
2020	Luiz Mario Lustosa Pascoal, Hugo Alexandre Dantas do Nascimento, Thierson Couto Rosa, Edjalma Queiroz da Silva, Everton Lima Aleixo	Using String-Comparison Measures to Improve and Evaluate Collaborative Filtering Recommender Systems.	BIAS
2020	Tobias D. Krafft, Marc P. Hauer, Katharina Anna Zweig	Why Do We Need to Be Bots? What Prevents Society from Detecting Biases in Recommendation Systems.	BIAS		news category
2019	Anish Anil Patankar, Joy Bose, Harshit Khanna	A Bias Aware News Recommendation System.	ICSC
2019	Dimitris Sacharidis, Kyriakos Mouratidis, Dimitrios Kleftogiannis	A Common Approach for Consumer and Provider Fairness in Recommendations.	RecSys (Late-Breaking Results)	movielens
2019	Rafael Gomes Mantovani, André L. D. Rossi, Edesio Alcobaça, Joaquin Vanschoren, André C. P. L. F. de Carvalho	A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers.	Inf. Sci.
2019	Duen-Ren Liu, Yu-Shan Liao, Ya-Han Chung, Kuan-Yu Chen	Advertisement recommendation based on personal interests and ad push fairness.	Kybernetes	NIUSNEWS	Individual
2019	Qi Wang, Jijun Yu, Weiwei Deng	An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations.	Electron. Commer. Res.
2019	Joseph Sirrianni, Xiaoqing Liu, Md Mahfuzer Rahman, Douglas Adams	An Opinion Diversity Enhanced Social Connection Recommendation Re-Ranking Method Based on Opinion Distance in Cyber Argumentation with Social Networking.	ICCC
2019	Changsheng Ma, Jianjun Li, Peng Pan, Guohui Li, Junbo Du	BDMF: A Biased Deep Matrix Factorization Model for Recommendation.	SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI
2019	Masoud Mansoury, Bamshad Mobasher, Robin Burke, Mykola Pechenizkiy	Bias Disparity in Collaborative Recommendation: Algorithmic Evaluation and Comparison.	RMSE@RecSys	Yelp (subset)	gender, business category,	masoudmansoury/yelp_core40 (github.com)
2019	Virginia Tsintzou, Evaggelia Pitoura, Panayiotis Tsaparas	Bias Disparity in Recommendation Systems.	RMSE@RecSys	Movielens 1M, synthetic	gender, genre
2019	Stefano Nembrini	Bias in the intervention in prediction measure in random forests: illustrations and recommendations.	Bioinform.
2019	Ruey-Cheng Chen, Qingyao Ai, Gaya Jayasinghe, W. Bruce Croft	Correcting for Recency Bias in Job Recommendation.	CIKM
2019	Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke	Crank up the Volume: Preference Bias Amplification in Collaborative Recommendation.	RMSE@RecSys	Movielens 1M	gender, genre
2019	Wenlong Sun, Sami Khenissi, Olfa Nasraoui, Patrick Shafto	Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering.	WWW (Companion Volume)	Synthetic	popularity
2019	Leonard Weydemann, Dimitris Sacharidis, Hannes Werthner	Defining and measuring fairness in location recommendations.	LocalRec@SIGSPATIAL	synthetic (from travel data solution)	Nationality, Popularity, item class, calibration
2019	Abraham Bernstein, Claes H. de Vreese, Natali Helberger, Wolfgang Schulz, Katharina Anna Zweig	Diversity, Fairness, and Data-Driven Personalization in (News) Recommender System (Dagstuhl Perspectives Workshop 19482).	Dagstuhl Reports
2019	Weiquan Wang, May D. Wang	Effects of Sponsorship Disclosure on Perceived Integrity of Biased Recommendation Agents: Psychological Contract Violation and Knowledge-Based Trust Perspectives.	Inf. Syst. Res.
2019	Suresh Kumar Gudla, Joy Bose, Koushik Reddy Sane	Enhanced Service Recommender and Ranking System Using Browsing Patterns of Users.	CCNC
2019	Rodrigo Borges, Kostas Stefanidis	Enhancing Long Term Fairness in Recommendations with Variational Autoencoders.	MEDES	movielens,netflix,Million Song Dataset	Ranking
2019	Abhijnan Chakraborty, Gourab K. Patro, Niloy Ganguly, Krishna P. Gummadi, Patrick Loiseau	Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations.	FAT	Adressa, Twitter (custom)	user activity
2019	Lucas Machado, Kostas Stefanidis	Fair Team Recommendations for Multidisciplinary Projects.	WI	DBLP	, Skills
2019	Michael D. Ekstrand, Robin Burke, Fernando Diaz	Fairness and discrimination in recommendation and retrieval.	RecSys
2019	Michael D. Ekstrand, Robin Burke, Fernando Diaz	Fairness and Discrimination in Retrieval and Recommendation.	SIGIR
2019	Yash Raj Shrestha, Yongjie Yang	Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems.	Algorithms
2019	Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow	Fairness in Recommendation Ranking through Pairwise Comparisons.	KDD	Custom (in-house)	generic (binary)
2019	Sahin Cem Geyik, Stuart Ambler, Krishnaram Kenthapadi	Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search.	KDD	Simulations synthetic	Age, Gender
2019	Bashir Rastegarpanah, Krishna P. Gummadi, Mark Crovella	Fighting Fire with Fire: Using Antidote Data to Improve Polarization and Fairness of Recommender Systems.	WSDM	Movielens	genre
2019	David Lee, Seolha Lee	Inferring the character of urban commercial areas from age-biased online search results: how place recommendation data can reveal dynamic seoul neighborhoods.	UbiComp/ISWC Adjunct
2019	Nasim Sonboli, Robin Burke	Localized Fairness in Recommender Systems.	UMAP (Adjunct Publication)	kiva.org	local conditions
2019	Himan Abdollahpouri, Robin Burke, Bamshad Mobasher	Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking.	FLAIRS Conference
2019	Himan Abdollahpouri, Robin Burke	Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness.	RMSE@RecSys
2019	Andres Ferraro	Music cold-start and long-tail recommendation: bias in deep representations.	RecSys	Million Song Dataset	, artist, genre
2019	Andreu Vall, Massimo Quadrana, Markus Schedl, Gerhard Widmer	Order, context and popularity bias in next-song recommendations.	Int. J. Multim. Inf. Retr.
2019	Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang	PAL: a position-bias aware learning framework for CTR prediction in live recommender systems.	RecSys
2019	Xinyi Li, Yifan Chen, Benjamin Pettit, Maarten de Rijke	Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior.	ACM Trans. Inf. Syst.
2019	Changhua Pei, Yi Zhang, Yongfeng Zhang, Fei Sun, Xiao Lin, Hanxiao Sun, Jian Wu, Peng Jiang, Junfeng Ge, Wenwu Ou, Dan Pei	Personalized re-ranking for recommendation.	RecSys
2019	Andreas G. Arens-Volland, Patrick Gratz, Alexandre Baudet, Louis Deladiennée, Marie Gallais, Yannick Naudet	Personalized Recommender System for Improving Gender-fairness in Teaching.	SMAP
2019	Himan Abdollahpouri	Popularity Bias in Ranking and Recommendation.	AIES
2019	Hanbo Deng, Lizhi Peng, Haibo Zhang, Bo Yang, Zhenxiang Chen	Ranking-based biased learning swarm optimizer for large-scale optimization.	Inf. Sci.
2019	Yashar Deldjoo, Vito Walter Anelli, Hamed Zamani, Alejandro Bellogín Kouki, Tommaso Di Noia	Recommender Systems Fairness Evaluation via Generalized Cross Entropy.	RMSE@RecSys	Xing job, Amazon review	interactions, interactions, premium memberships
2019	Xinyang Yi, Ji Yang, Lichan Hong, Derek Zhiyuan Cheng, Lukasz Heldt, Aditee Kumthekar, Zhe Zhao, Li Wei, Ed H. Chi	Sampling-bias-corrected neural modeling for large corpus item recommendations.	RecSys
2019	Pascal Monestiez, Christophe Botella	Species Recommendation using Intensity Models and Sampling Bias Correction (GeoLifeCLEF 2019: Lof_of_Lof team).	CLEF (Working Notes)
2019	Ludovico Boratto, Gianni Fenu, Mirko Marras	The Effect of Algorithmic Bias on Recommender Systems for Massive Open Online Courses.	ECIR (1)
2019	Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher	The Unfairness of Popularity Bias in Recommendation.	RMSE@RecSys	movielens	interaction, popularity
2019	Dimitris Sacharidis	Top-N group recommendations with fairness.	SAC	Movielens1M
2019	Chunhua Sun, Yinjie Xu	Topic Model-Based Recommender System for Longtailed Products Against Popularity Bias.	DSC
2019	Divyaa L. R., Nargis Pervin	Towards generating scalable personalized recommendations: Integrating social trust, social bias, and geo-spatial clustering.	Decis. Support Syst.
2019	Muhammad Shoaib Ikram, Anban W. Pillay, Edgar Jembere	Using social networks to enhance a deep learning approach to solve the cold-start problem in recommender systems.	FAIR
2019	Bin Xia, Junjie Yin, Jian Xu, Yun Li	WE-Rec: A fairness-aware reciprocal recommendation based on Walrasian equilibrium.	Knowl. Based Syst.	WUZZUF job posts, speed dating experiment	category,gender, career,gender
2019	Sara Migliorini, Elisa Quintarelli, Damiano Carra, Alberto Belussi	What is the Role of Context in Fair Group Recommendations?	PIE@CAiSE	custom
2018	Jiao Dai, Mingming Li, Songlin Hu, Jizhong Han	A Hybrid Model Based on the Rating Bias and Textual Bias for Recommender Systems.	ICONIP (2)
2018	Michael D. Ekstrand, Mucun Tian, Ion Madrazo Azpiazu, Jennifer D. Ekstrand, Oghenemaro Anuyah, David McNeill, Maria Soledad Pera	All The Cool Kids, How Do They Fit In?: Popularity and Demographic Biases in Recommender Evaluation and Effectiveness.	FAT
2018	Bo Xiao, Izak Benbasat	An empirical examination of the influence of biased personalized product recommendations on consumers" decision making outcomes.	Decis. Support Syst.
2018	Robin Burke, Nasim Sonboli, Aldo Ordonez-Gauger	Balanced Neighborhoods for Multi-sided Fairness in Recommendation.	FAT	Movielens, Kiva.org	gender, geographic location
2018	Cangfeng Ding, Kan Li	Centrality ranking in multiplex networks using topologically biased random walks.	Neurocomputing
2018	Cangfeng Ding, Kan Li	Centrality Ranking via Topologically Biased Random Walks in Multiplex Networks.	IJCNN
2018	Jeronymo Mota Alves de Carvalho	Collaborative Mobile Ad Hoc Intrusion Detection System.	Jeronymo Mota Alves de Carvalho
2018	Edesio Alcobaça, Rafael Gomes Mantovani, André L. D. Rossi, André C. P. L. F. de Carvalho	Dimensionality Reduction for the Algorithm Recommendation Problem.	BRACIS
2018	Ye Yuan, Xin Luo, Mingsheng Shang	Effects of preprocessing and training biases in latent factor models for recommender systems.	Neurocomputing
2018	Iordanis Koutsopoulos, Maria Halkidi	Efficient and Fair Item Coverage in Recommender Systems.	DASC/PiCom/DataCom/CyberSciTech	Movielens	item rating?
2018	Yuanbo Xu, Yongjian Yang, Jiayu Han, En Wang, Fuzhen Zhuang, Hui Xiong	Exploiting the Sentimental Bias between Ratings and Reviews for Enhancing Recommendation.	ICDM
2018	Diego Carvalho, Nícollas Silva, Thiago Silveira, Fernando Mourão, Adriano C. M. Pereira, Diego Dias, Leonardo Rocha	FAiR: A Framework for Analyses and Evaluations on Recommender Systems.	ICCSA (3)
2018	Yong Zheng, Tanaya Dave, Neha Mishra, Harshit Kumar	Fairness In Reciprocal Recommendations: A Speed-Dating Study.	UMAP (Adjunct Publication)	speed-dating data	ace,gender,race,dating purpose
2018	Ziwei Zhu, Xia Hu, James Caverlee	Fairness-Aware Tensor-Based Recommendation.	CIKM	Movielens,Twitter dataset,syntetic	ace,gender,race,dating purpose
2018	Qiliang Zhu, Ao Zhou, Qibo Sun, Shangguang Wang, Fangchun Yang	FMSR: A Fairness-Aware Mobile Service Recommendation Method.	ICWS	Crawled from app store	popularity
2018	Arthur Flexer, Monika Dörfler, Jan Schlüter, Thomas Grill	Hubness as a Case of Technical Algorithmic Bias in Music Recommendation.	ICDM Workshops
2018	Helena Webb, Ansgar R. Koene, Menisha Patel, Elvira Perez Vallejos	Multi-Stakeholder Dialogue for Policy Recommendations on Algorithmic Fairness.	SMSociety
2018	Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, Jöran Beel	Position Bias in Recommender Systems for Digital Libraries.	iConference
2018	Lucas Marcondes Pavelski, Marie-Eléonore Kessaci, Myriam Regattieri Delgado	Recommending Meta-Heuristics and Configurations for the Flowshop Problem via Meta-Learning: Analysis and Design.	BRACIS
2018	Maria Stratigi, Haridimos Kondylakis, Kostas Stefanidis	The FairGRecs Dataset: A Dataset for Producing Health-related Recommendations.	SWH@ISWC			MariaStratigi / fairgrecs-dataset — Bitbucket
2018	Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, Fernando Diaz	Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems.	CIKM	custom (maybe internal, from Spotify)	Artist popularity
2018	Behnoush Abdollahi, Olfa Nasraoui	Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems.	Human and Machine Learning
2018	Jurek Leonhardt, Avishek Anand, Megha Khosla	User Fairness in Recommender Systems.	WWW (Companion Volume)	Movielens
2018	Chen Karako, Putra Manggala	Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations.	UMAP (Adjunct Publication)	Burst	gender
2017	Allen J. Fairchild, Simon P. Campion, Arturo S. García, Robin Wolff, Terrence Fernando, David J. Roberts	A Mixed Reality Telepresence System for Collaborative Space Operation.	IEEE Trans. Circuits Syst. Video Technol.
2017	Sirui Yao, Bert Huang	Beyond Parity: Fairness Objectives for Collaborative Filtering.	NIPS	Synthetic, Movielens1M	gender
2017	Deqiang Kong, Jingyuan Tang, Zhenfeng Zhu, Jian Cheng, Yao Zhao	De-biased dart ensemble model for personalized recommendation.	ICME
2017	Jifeng Xuan, He Jiang, Hongyu Zhang, Zhilei Ren	Developer recommendation on bug commenting: a ranking approach for the developer crowd.	Sci. China Inf. Sci.
2017	Ye Yuan, Xin Luo, Mingsheng Shang, Xin-Yi Cai	Effect of linear biases in latent factor models on high-dimensional and sparse matrices from recommender systems.	ICNSC
2017	Shenghao Liu, Bang Wang, Minghua Xu	Event Recommendation based on Graph Random Walking and History Preference Reranking.	SIGIR
2017	Pierre-René Lhérisson, Fabrice Muhlenbach, Pierre Maret	Fair Recommendations Through Diversity Promotion.	ADMA	Movielens,Last.fm	artists	Dropbox - R_ADMA - Semplifica la tua vita
2017	Natwar Modani, Deepali Jain, Ujjawal Soni, Gaurav Kumar Gupta, Palak Agarwal	Fairness Aware Recommendations on Behance.	PAKDD (2)
2017	Maria Stratigi, Haridimos Kondylakis, Kostas Stefanidis	Fairness in Group Recommendations in the Health Domain.	ICDE
2017	Dimitris Serbos, Shuyao Qi, Nikos Mamoulis, Evaggelia Pitoura, Panayiotis Tsaparas	Fairness in Package-to-Group Recommendations.	WWW	[Yelp]
2017	Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, Shaoping Ma	Fairness-Aware Group Recommendation with Pareto-Efficiency.	RecSys	[Movielens1M, MoviePilot]
2017	Ankesh Anand, Tanmoy Chakraborty, Amitava Das	FairScholar: Balancing Relevance and Diversity for Scientific Paper Recommendation.	ECIR
2017	Wenmin Wu, Jianli Zhao, Chunsheng Zhang, Fang Meng, Zeli Zhang, Yang Zhang, Qiuxia Sun	Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding.	Knowl. Based Syst.
2017	Xiaoying Zhang, Junzhou Zhao, John C. S. Lui	Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations.	RecSys
2017	Sushma Channamsetty, Michael D. Ekstrand	Recommender Response to Diversity and Popularity Bias in User Profiles.	FLAIRS Conference
2017	Ahmed Saleh, Florian Mai, Chifumi Nishioka, Ansgar Scherp	Reranking-based Recommender System with Deep Learning.	GI-Jahrestagung
2017	Alejandro Bellogín, Pablo Castells, Iván Cantador	Statistical biases in Information Retrieval metrics for recommender systems.	Inf. Retr. J.
2017	Abhijnan Chakraborty, Johnnatan Messias, Fabrício Benevenuto, Saptarshi Ghosh, Niloy Ganguly, Krishna P. Gummadi	Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations.	ICWSM	Custom	gender, race, age
2017	Elvira Perez Vallejos, Ansgar R. Koene, Virginia Portillo, Liz Dowthwaite, Monica Cano	Young People"s Policy Recommendations on Algorithm Fairness.	WebSci
2016	Yongjun Ye, Peng Li, Rui Li, Meilin Zhou, Yifang Wan, Bin Wang	Ranking Microblog Users via URL Biased Posts.	WISE (2)
2016	Dien L. Nguyen, Tung M. Le	Recommendation system for Facebook public events based on probabilistic classification and re-ranking.	KSE
2016	Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims	Recommendations as Treatments: Debiasing Learning and Evaluation.	ICML
2016	Mazen Alsarem	Semantic Snippets via Query-Biased Ranking of Linked Data Entities. (Snippets Sémantiques par un ordonnancement biaisé-requête d"entités de données liées).	Mazen Alsarem
2016	Tobias Schnabel, Adith Swaminathan, Peter I. Frazier, Thorsten Joachims	Unbiased Comparative Evaluation of Ranking Functions.	ICTIR
2016	Vladimir Bobrikov, Elena Nenova, Dmitry I. Ignatov	What is a Fair Value of Your Recommendation List?	EEML@CLA
2015	Jui-Hung Chang, Chin-Feng Lai, Ming-Shi Wang	A fair scheduler using cloud computing for digital TV program recommendation system.	Telecommun. Syst.
2015	Marcos Aurélio Domingues, Camila Vaccari Sundermann, Flávio M. M. Barros, Marcelo G. Manzato, Maria G. C. Pimentel, Solange Oliveira Rezende	Applying multi-view based metadata in personalized ranking for recommender systems.	SAC
2015	Péter Biró, László Á. Kóczy, Balázs Sziklai	Fair apportionment in the view of the Venice Commission"s recommendation.	Math. Soc. Sci.
2015	Milad Shokouhi, Qi Guo	From Queries to Cards: Re-ranking Proactive Card Recommendations Based on Reactive Search History.	SIGIR
2015	Kotaro Sakamoto, Hideyuki Shibuki, Tatsunori Mori, Noriko Kando	Fusion of Heterogeneous Information in Graph-Based Ranking for Query-Biased Summarization.	GSB@SIGIR
2015	Fred Baker, Godred Fairhurst	IETF Recommendations Regarding Active Queue Management.	RFC
2015	Ives Rey-Otero, Mauricio Delbracio	Is Repeatability an Unbiased Criterion for Ranking Feature Detectors?	SIAM J. Imaging Sci.
2015	Rafael Gomes Mantovani, André L. D. Rossi, Joaquin Vanschoren, André C. P. L. F. de Carvalho	Meta-learning Recommendation of Default Hyper-parameter Values for SVMs in Classification Tasks.	MetaSel@PKDD/ECML
2015	Ridho Reinanda, Edgar Meij, Maarten de Rijke	Mining, Ranking and Recommending Entity Aspects.	SIGIR
2015	Filippo Bistaffa, Alessandro Farinelli, Georgios Chalkiadakis, Sarvapali D. Ramchurn	Recommending Fair Payments for Large-Scale Social Ridesharing.	RecSys
2015	Shiyou Qian, Jian Cao, Frédéric Le Mouël, Issam Sahel, Minglu Li	SCRAM: A Sharing Considered Route Assignment Mechanism for Fair Taxi Route Recommendations.	KDD
2015	Rafael Gomes Mantovani, André Luis Debiaso Rossi, Joaquin Vanschoren, Bernd Bischl, André C. P. L. F. de Carvalho	To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning.	IJCNN
2017	Robin Burke, Nasim Sonboli, Masoud Mansoury, Aldo Ordonez-Gauger	Balanced Neighborhoods for Fairness-Aware Collaborative Recommendation	FATREC	ML1M	gender
2017	Christopher Riederer, Augustin Chaintreau	The Price of Fairness in Location Based Advertising	FATREC	Instagram	gender, race, location, revenue
2017	Abhijnan Chakraborty, Aniko Hannak, Asia J. Biega, Krishna P. Gummadi	Fair Sharing for Sharing Economy Platforms	FATREC		Provider attribute
2017	Piotr Sapiezynski, Valentin Kassarnig, Christo Wilson	Academic performance prediction in a gender-imbalanced environment	FATREC
2018	Chen Karako, Putra Manggala	Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations	FATREC	Burst/Shopify
2018	Ziwei Zhu, Jianling Wang, Yin Zhang, James Caverlee	Fairness-Aware Recommendation of Information Curators	FATREC	Twitter	race
2018	Golnoosh Farnadi, Pigi Kouki, Spencer K. Thompson, Sriram Srinivasan, Lise Getoor	A Fairness-aware Hybrid Recommender System	FATREC	ML1M	gender, age, occupation
2018	Weiwen Liu, Robin Burke	Personalizing Fairness-aware Re-ranking	FATREC	ML1M, FimTrust, Kiva	Provider attribute
2018	Jiahao Chen	Fair lending needs explainable models for responsible recommendation	FATREC
2020	Harshal A. Chaudhari, Sangdi Lin, Ondrej Linda	A General Framework for Fairness in Multistakeholder Recommendations	FAccTRec	Zillow	Provider attribute
2020	Nasim Sonboli, Robin Burke, Nicholas Mattei, Farzad Eskandanian, Tian Gao	"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation	FAccTRec	Movielens, Kiva	, activity, country, gender,
2020	Mukund Telukunta, Venkata Sriram Siddhardh Nadendla	On the Identification of Fair Auditors to Evaluate Recommender Systems based on a Novel Non-Comparative Fairness Notion	FAccTRec		Consumer attribute
2020	Amifa Raj, Michael D. Ekstrand	Comparing Fair Ranking Metrics	FAccTRec
2020	CHARLES DICKENS, RISHIKA SINGH, LISE GETOOR	HyperFair: A Soft Approach to Integrating Fairness Criteria	FAccTRec	ML1M	gender, item genre
2020	G Roshan Lal, Sahin Cem Geyik, Krishnaram Kenthapadi	Fairness-Aware Online Personalization	FAccTRec
2020	Harshal A. Chaudhari, Sangdi Lin, Ondrej Linda	A General Framework for Fairness in Multistakeholder Recommendations	FAccTRec
2021	Shiri Dori-Hacohen, Roberto Montenegro, Fabricio Murai, Scott A. Hale, Keen Sung, Michela Blain, Jennifer Edwards-Johnson	Recommendation Fairness: From Static to Dynamic	FAccTRec
2021	Shiri Dori-Hacohen, Roberto Montenegro, Fabricio Murai, Scott A. Hale, Keen Sung, Michela Blain, Jennifer Edwards-Johnson	Fairness via AI: Bias Reduction in Medical Information	FAccTRec
2021	Agoritsa Polyzou, Maria Kalantzi, George Karypis	FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation	FAccTRec	synthetic, Minnesota	socioeconomic
2022	Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, Luca Belli	Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems	FAccTRec	synthetic, German	demographic
2022	Allen Lin, Ziwei Zhu, Jianling Wang, James Caverlee	Towards Fair Conversational Recommender Systems	FAccTRec	Lastfm
2022	Masoud Mansoury, Bamshad Mobasher, Herke van Hoof	Exposure-Aware Recommendation using Contextual Bandits	FAccTRec	Amazon, Movielens	genres
2022	Riku Togashi, Kenshi Abe	Fair Matrix Factorisation for Large-Scale Recommender Systems	FAccTRec	ML20M, MDS	Provider attribute
2022	Michael D. Ekstrand, Maria Soledad Pera	Matching Consumer Fairness Objectives & Strategies for RecSys	FAccTRec		Consumer attribute
2022	Karlijn Dinnissen, Christine Bauer	A Stakeholder-Centered View on Fairness in Music Recommender Systems	FAccTRec
2022	Paresha Farastu, Nicholas Mattei, Robin Burke	Who Pays? Personalization, Bossiness and the Cost of Fairness	FAccTRec		Consumer attribute
2022	Rebecca Salganik, Fernando Diaz, Golnoosh Farnadi	Analyzing the Effect of Sampling in GNNs on Individual Fairness	FAccTRec	BlogCatalog, Flickr	Consumer attribute
2022	Mirae Kim, Simon Woo	Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design	FAccTRec
2022	Jessie J. Smith, Lex Beattie	RecSys Fairness Metrics: Many to Use But Which One To Choose?	FAccTRec
2015	Tobias Schnabel, Adith Swaminathan, Thorsten Joachims	Unbiased Ranking Evaluation on a Budget.	WWW (Companion Volume)