Fetching the paper…
Reading the bibliography…
With the increasing use and impact of recommender systems in our daily lives, how to achieve fairness in recommendation has become an important problem.
Comprehensive fair meta-learned recommender system. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1989–1999
Tianxin Wei and Jingrui He. 2022 · 1999
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2006 · 2006
Earlier work this paper cites.
Probabilistic matrix factorization. In Advances in neural information processing systems . 1257–1264
Andriy Mnih and Russ R Salakhutdinov. 2008 · 2008
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th annual international conference on machine learning . 617–624
Bin Li, Qiang Yang, and Xiangyang Xue. 2009 · 2009
Earlier work this paper cites.
Improving aggregate recommendation diversity using ranking-based techniques
Gediminas Adomavicius and YoungOk Kwon. 2011 · 2011
Earlier work this paper cites.
Precision-oriented evaluation of recommender systems: an algorithmic comparison. In Proceedings of the fifth ACM conference on Recommender systems . 333–336
Alejandro Bellogin, Pablo Castells, and Ivan Cantador. 2011 · 2011
Earlier work this paper cites.
Introduction to recommender systems handbook
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011 · 2011
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.
Transfer learning in heterogeneous collaborative filtering domains
Weike Pan and Qiang Yang. 2013 · 2013
Earlier work this paper cites.
Active transfer learning for cross-system recommendation. In Twenty-Seventh AAAI Conference on Artificial Intelligence
Lili Zhao, Sinno Jialin Pan, Evan Wei Xiang, Erheng Zhong, Zhongqi Lu, and Qiang Yang. 2013 · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Correcting Popularity Bias by Enhancing Recommendation Neutrality. In RecSys
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Learning Fair Classifiers
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi. 2015 · 2015
Earlier work this paper cites.
Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems . 7–10
Heng-Tze Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, et al · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning. In NeurIPS . 3315–3323
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
Earlier work this paper cites.
“Told you i didn’t like it”: Exploiting uninteresting items for effective collaborative filtering. In 2016 IEEE 32nd International Conference on Data Engineering (ICDE) . IEEE, 349–360
Won-Seok Hwang, Juan Parc, Sang-Wook Kim, Jongwuk Lee, and Dongwon Lee. 2016 · 2016
Earlier work this paper cites.
Causal inference in statistics: A primer
Judea Pearl, Madelyn Glymour, and Nicholas P Jewell. 2016 · 2016
Earlier work this paper cites.
Controlling popularity bias in learning-to-rank recommendation. In RecSys
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 2017 · 2017
Earlier work this paper cites.
Multisided fairness for recommendation
Robin Burke. 2017 · 2017
Earlier work this paper cites.
Counterfactual fairness. In NeurIPS . 4069–4079
Matt Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. 2017 · 2017
Earlier work this paper cites.
Chao Lan and Jun Huan. 2017 · 2017
Cited alongside, same era.
Fairness-aware group recommendation with pareto-efficiency. In Proceedings of the Eleventh ACM Conference on Recommender Systems . 107–115
Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, and Shaoping Ma. 2017 · 2017
Cited alongside, same era.
Cross-domain recommendation: An embedding and mapping approach.. In IJCAI , Vol. 17. 2464–2470
Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017 · 2017
Cited alongside, same era.
Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval . 515–524
Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017 · 2017
Cited alongside, same era.
Deep Matrix Factorization Models for Recommender Systems.. In IJCAI
Adversarial Learning for Debiasing Knowledge Graph Embeddings
Mario Arduini, Lorenzo Noci, Federico Pirovano, Ce Zhang, Yash Raj Shrestha, and Bibek Paudel. 2020 · 2020
Later among the works it cites.
A fair classifier using mutual information. In ISIT . IEEE, 2521–2526
Jaewoong Cho, Gyeongjo Hwang, and Changho Suh. 2020 · 2020
Later among the works it cites.
Fairness in deep learning: A computational perspective
Mengnan Du, Fan Yang, Na Zou, and Xia Hu. 2020 · 2020
Later among the works it cites.
ATLRec: An attentional adversarial transfer learning network for cross-domain recommendation
Ying Li, Jia-Jie Xu, Peng-Peng Zhao, Jun-Hua Fang, Wei Chen, and Lei Zhao. 2020 · 2020
Later among the works it cites.
Meta-learning on heterogeneous information networks for cold-start recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1563–1573
Yuanfu Lu, Yuan Fang, and Chuan Shi. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017 · 2017
Cited alongside, same era.
Beyond parity: Fairness objectives for collaborative filtering. In Advances in Neural Information Processing Systems . 2921–2930
Sirui Yao and Bert Huang. 2017 · 2017
Cited alongside, same era.
Adversarial removal of demographic attributes from text data
Yanai Elazar and Yoav Goldberg. 2018 · 2018
Cited alongside, same era.
User Fairness in Recommender Systems. In Companion Proceedings WWW . 101–102
Jurek Leonhardt, Avishek Anand, and Megha Khosla. 2018 · 2018
Cited alongside, same era.
Learning adversarially fair and transferable representations. In International Conference on Machine Learning . PMLR, 3384–3393
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel. 2018 · 2018
Cited alongside, same era.
Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In CIKM
R. Mehrotra, J. McInerney, H. Bouchard, M. Lalmas, and F. Diaz. 2018 · 2018
Cited alongside, same era.
Spectral Normalization for Generative Adversarial Networks. In International Conference on Learning Representations
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018 · 2018
Cited alongside, same era.
Multi-stakeholder recommendation and its connection to multi-sided fairness
Himan Abdollahpouri and Robin Burke. 2019 · 2019
Cited alongside, same era.
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms. In WWW
Gourab K Patro, Arpita Biswas, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. 2020 · 2020
Later among the works it cites.
Neural Logic Reasoning. In CIKM . 1365–1374
Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, and Yongfeng Zhang. 2020 · 2020
Later among the works it cites.
Cross-Domain Recommendation with Adversarial Examples. In International Conference on Database Systems for Advanced Applications . Springer, 573–589
Haoran Yan, Pengpeng Zhao, Fuzhen Zhuang, Deqing Wang, Yanchi Liu, and Victor S Sheng. 2020 · 2020
Later among the works it cites.
Joint transfer of model knowledge and fairness over domains using wasserstein distance
Taeho Yoon, Jaewook Lee, and Woojin Lee. 2020 · 2020
Later among the works it cites.
Revisiting Alternative Experimental Settings for Evaluating Top-N Item Recommendation Algorithms
Wayne Xin Zhao, Junhua Chen, Pengfei Wang, Qi Gu, and Ji-Rong Wen. 2020 · 2020
Later among the works it cites.
Neural Collaborative Reasoning
Hanxiong Chen, Shaoyun Shi, Yunqi Li, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Towards Long-term Fairness in Recommendation
Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation
Chenglin Li, Mingjun Zhao, Huanming Zhang, Chenyun Yu, Lei Cheng, Guoqiang Shu, Beibei Kong, and Di Niu. 2021c · 2021
Later among the works it cites.
User-oriented Fairness in Recommendation
Yunqi Li, Hanxiong Chen, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2021a · 2021
Later among the works it cites.
Investigating gender fairness of recommendation algorithms in the music domain
Alessandro B Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, Stefan Brandl, Oleg Lesota, and Markus Schedl. 2021 · 2021
Later among the works it cites.
Fairness in Recommender Systems: Research Landscape and Future Directions
Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin, Alessandro Diffonzo, and Dario Zanzonelli. 2022 · 2022
Later among the works it cites.
Fairness in Information Access Systems
Michael D Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz, et al · 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. 2022 · 2022
Later among the works it cites.
A survey on the fairness of recommender systems
Yifan Wang, Weizhi Ma, Min Zhang*, Yiqun Liu, and Shaoping Ma. 2022 · 2022
Later among the works it cites.
Are Big Recommendation Models Fair to Cold Users?
Chuhan Wu, Fangzhao Wu, Tao Qi, and Yongfeng Huang. 2022 · 2022
Later among the works it cites.
Fairness in ranking, part ii: Learning-to-rank and recommender systems
Meike Zehlike, Ke Yang, and Julia Stoyanovich. 2022 · 2022
Later among the works it cites.
Facing the cold start problem in recommender systems
Blerina Lika, Kostas Kolomvatsos, and Stathes Hadjiefthymiades. 2014 · 2073
Closest in time.