Fetching the paper…
Reading the bibliography…
In social recommender systems, it is crucial that the recommendation models provide equitable visibility for different demographic groups, such as gender or race.
The proof and measurement of association between two things
Charles Spearman. 1987 · 1987
Earlier work this paper cites.
Emergence of scaling in random networks
Albert-László Barabási and Réka Albert. 1999 · 1999
Earlier work this paper cites.
Community evolution in dynamic multi-mode networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining . 677–685
Lei Tang, Huan Liu, Jianping Zhang, and Zohreh Nazeri. 2008 · 2008
Earlier work this paper cites.
For the few not the many? The effects of affirmative action on presence, prominence, and social capital of women directors in Norway
Cathrine Seierstad and Tore Opsahl. 2011 · 2011
Earlier work this paper cites.
Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference . 214–226
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012 · 2012
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Earlier work this paper cites.
Fairness-aware loan recommendation for microfinance services. In Proceedings of the 2014 international conference on social computing . 1–4
Eric L Lee, Jing-Kai Lou, Wei-Ming Chen, Yen-Chi Chen, Shou-De Lin, Yen-Sheng Chiang, and Kuan-Ta Chen. 2014 · 2014
Earlier work this paper cites.
node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . 855–864
Aditya Grover and Jure Leskovec. 2016 · 2016
Earlier work this paper cites.
Inferring gender from names on the web: A comparative evaluation of gender detection methods. In Proceedings of the 25th International conference companion on World Wide Web . 53–54
Fariba Karimi, Claudia Wagner, Florian Lemmerich, Mohsen Jadidi, and Markus Strohmaier. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Twitter’s glass ceiling: The effect of perceived gender on online visibility. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 10. 289–298
Shirin Nilizadeh, Anne Groggel, Peter Lista, Srijita Das, Yong-Yeol Ahn, Apu Kapadia, and Fabio Rojas. 2016 · 2016
Earlier work this paper cites.
The effect of recommendations on network structure. In Proceedings of the 25th international conference on World Wide Web . 1157–1167
Jessica Su, Aneesh Sharma, and Sharad Goel. 2016 · 2016
Earlier work this paper cites.
Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web . 173–182
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
Earlier work this paper cites.
Fairness-aware group recommendation with pareto-efficiency. In Proceedings of the eleventh ACM conference on recommender systems . 107–115
Lin Xiao, Zhang Min, Zhang Yongfeng, Gu Zhaoquan, Liu Yiqun, and Ma Shaoping. 2017 · 2017
Earlier work this paper cites.
Beyond parity: Fairness objectives for collaborative filtering
Sirui Yao and Bert Huang. 2017 · 2017
Earlier work this paper cites.
Joint representation learning for top-n recommendation with heterogeneous information sources. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1449–1458
Yongfeng Zhang, Qingyao Ai, Xu Chen, and W Bruce Croft. 2017 · 2017
Earlier work this paper cites.
Investigating the impact of gender on rank in resume search engines. In Proceedings of the 2018 chi conference on human factors in computing systems . 1–14
Le Chen, Ruijun Ma, Anikó Hannák, and Christo Wilson. 2018 · 2018
Earlier work this paper cites.
A fairness-aware hybrid recommender system
Golnoosh Farnadi, Pigi Kouki, Spencer K Thompson, Sriram Srinivasan, and Lise Getoor. 2018 · 2018
Earlier work this paper cites.
A short-term intervention for long-term fairness in the labor market. In Proceedings of the 2018 World Wide Web Conference . 1389–1398
Lily Hu and Yiling Chen. 2018 · 2018
Cited alongside, same era.
Homophily influences ranking of minorities in social networks
Fariba Karimi, Mathieu Génois, Claudia Wagner, Philipp Singer, and Markus Strohmaier. 2018 · 2018
Cited alongside, same era.
Delayed impact of fair machine learning. In International Conference on Machine Learning . PMLR, 3150–3158
Lydia T Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. 2018 · 2018
Cited alongside, same era.
Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity. In Proceedings of the 2018 World Wide Web Conference . 923–932
Ana-Andreea Stoica, Christopher Riederer, and Augustin Chaintreau. 2018 · 2018
Cited alongside, same era.
On discrimination discovery and removal in ranked data using causal graph. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2536–2544
Algorithmic Fairness in Education
René F. Kizilcec and Hansol Lee. 2020 · 2020
Later among the works it cites.
Bursting the filter bubble: Fairness-aware network link prediction. In Proceedings of the AAAI conference on artificial intelligence , Vol. 34. 841–848
Farzan Masrour, Tyler Wilson, Heng Yan, Pang-Ning Tan, and Abdol Esfahanian. 2020 · 2020
Later among the works it cites.
Fairrec: Two-sided fairness for personalized recommendations in two-sided platforms. In Proceedings of the web conference 2020 . 1194–1204
Gourab K Patro, Arpita Biswas, Niloy Ganguly, Krishna P Gummadi, and Abhijnan Chakraborty. 2020 · 2020
Later among the works it cites.
Towards long-term fairness in recommendation. In Proceedings of the 14th ACM international conference on web search and data mining . 445–453
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, et al · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yongkai Wu, Lu Zhang, and Xintao Wu. 2018 · 2018
Cited alongside, same era.
Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018
Cited alongside, same era.
The unfairness of popularity bias in recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019 · 2019
Cited alongside, same era.
Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 2212–2220
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al · 2019
Cited alongside, same era.
Compositional fairness constraints for graph embeddings. In International Conference on Machine Learning . PMLR, 715–724
Avishek Bose and William Hamilton. 2019 · 2019
Cited alongside, same era.
Fairwalk: towards fair graph embedding. In Proceedings of the 28th International Joint Conference on Artificial Intelligence . 3289–3295
Tahleen Rahman, Bartlomiej Surma, Michael Backes, and Yang Zhang. 2019 · 2019
Cited alongside, same era.
Neural graph collaborative filtering. In Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval . 165–174
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019 · 2019
Cited alongside, same era.
Group retention when using machine learning in sequential decision making: the interplay between user dynamics and fairness
Xueru Zhang, Mohammadmahdi Khaliligarekani, Cem Tekin, et al · 2019
Cited alongside, same era.
User-oriented fairness in recommendation. In Proceedings of the Web Conference 2021 . 624–632
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Weiwen Liu, Feng Liu, Ruiming Tang, Ben Liao, Guangyong Chen, and Pheng Ann Heng. 2021 · 2021
Later among the works it cites.
Fairdrop: Biased edge dropout for enhancing fairness in graph representation learning
Indro Spinelli, Simone Scardapane, Amir Hussain, and Aurelio Uncini. 2021 · 2021
Later among the works it cites.
Fairness-aware news recommendation with decomposed adversarial learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 35. 4462–4469
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021 · 2021
Later among the works it cites.
Fair representation learning for heterogeneous information networks. In Proceedings of the International AAAI Conference on Web and Social Media , Vol. 15. 877–887
Ziqian Zeng, Rashidul Islam, Kamrun Naher Keya, James Foulds, Yangqiu Song, and Shimei Pan. 2021 · 2021
Later among the works it cites.
Recommendation fairness: From static to dynamic
Dell Zhang and Jun Wang. 2021 · 2021
Later among the works it cites.
Long-term Dynamics of Fairness Intervention in Connection Recommender Systems
Nil-Jana Akpinar, Cyrus DiCiccio, Preetam Nandy, and Kinjal Basu. 2022 · 2022
Later among the works it cites.
Toward Pareto efficient fairness-utility trade-off in recommendation through reinforcement learning. In Proceedings of the fifteenth ACM international conference on web search and data mining . 316–324
Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, and Yongfeng Zhang. 2022 · 2022
Later among the works it cites.
Achieving long-term fairness in sequential decision making. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 9549–9557
Yaowei Hu and Lu Zhang. 2022 · 2022
Later among the works it cites.
Achieving Counterfactual Fairness for Causal Bandit. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 36. 6952–6959
Wen Huang, Lu Zhang, and Xintao Wu. 2022 · 2022
Later among the works it cites.
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks. In 2022 IEEE International Conference on Data Mining (ICDM) . IEEE, 975–980
Hussain Hussain, Meng Cao, Sandipan Sikdar, Denis Helic, Elisabeth Lex, Markus Strohmaier, and Roman Kern. 2022 · 2022
Later among the works it cites.
Fairness in recommendation: A survey
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022a · 2022
Later among the works it cites.
RecBole 2.0: Towards a More Up-to-Date Recommendation Library. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management . 4722–4726
Wayne Xin Zhao, Yupeng Hou, Xingyu Pan, Chen Yang, Zeyu Zhang, Zihan Lin, Jingsen Zhang, Shuqing Bian, Jiakai Tang, Wenqi Sun, et al · 2022
Later among the works it cites.