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Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model disparity in recommendation.
Using “annotator rationales” to improve machine learning for text categorization. In Human language technologies 2007: The conference of the North American chapter of the association for computational linguistics; proceedings of the main conference . 260–267
Omar Zaidan, Jason Eisner, and Christine Piatko. 2007 · 2007
Earlier work this paper cites.
Learning to rank features for recommendation over multiple categories. In SIGIR
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016 · 2016
Earlier work this paper cites.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web . 507–517
Ruining He and Julian McAuley. 2016 · 2016
Earlier work this paper cites.
17. A value for n-person games
Lloyd S Shapley. 2016 · 2016
Earlier work this paper cites.
Controlling popularity bias in learning-to-rank recommendation. In Proceedings of the eleventh ACM conference on recommender systems . 42–46
Himan Abdollahpouri, Robin Burke, and Bamshad Mobasher. 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
Xiao Lin, Min Zhang, Yongfeng Zhang, Zhaoquan Gu, Yiqun Liu, and Shaoping Ma. 2017 · 2017
Earlier work this paper cites.
Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In RecSys
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017 · 2017
Earlier work this paper cites.
Beyond Parity: Fairness Objectives for Collaborative Filtering. In Advances in Neural Information Processing Systems
Sirui Yao and Bert Huang. 2017 · 2017
Earlier work this paper cites.
From Parity to Preference-Based Notions of Fairness in Classification. In Proceedings of NIPS’17
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P. Gummadi, and Adrian Weller. 2017 · 2017
Earlier work this paper cites.
Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation
Qingyao Ai, Vahid Azizi, Xu Chen, and Yongfeng Zhang. 2018 · 2018
Earlier work this paper cites.
Balanced Neighborhoods for Multi-sided Fairness in Recommendation. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (Proceedings of Machine Learning Research) , Sorelle A. Friedler and Christo Wilson (Eds.), Vol. 81. PMLR, New York, NY, USA, 202–214
Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018 · 2018
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
Le Chen, Ruijun Ma, Anikó Hannák, and Christo Wilson. 2018 · 2018
Earlier work this paper cites.
Does mitigating ML's impact disparity require treatment disparity?. In Advances in Neural Information Processing Systems . Curran Associates, Inc
Zachary Lipton, Julian McAuley, and Alexandra Chouldechova. 2018 · 2018
Earlier work this paper cites.
Towards a Fair Marketplace: Counterfactual Evaluation of the Trade-off Between Relevance, Fairness & Satisfaction in Recommendation Systems. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management
Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018 · 2018
Earlier work this paper cites.
Fairness of Exposure in Rankings. In Proceedings of the 24th ACM SIGKDD
Ashudeep Singh and Thorsten Joachims. 2018 · 2018
Earlier work this paper cites.
Ziwei Zhu, Xia Hu, and James Caverlee. 2018 · 2018
Earlier work this paper cites.
The unfairness of popularity bias in recommendation
Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019 · 2019
Earlier work this paper cites.
Transparent, scrutable and explainable user models for personalized recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval . 265–274
Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019 · 2019
Earlier work this paper cites.
Fairness in recommendation ranking through pairwise comparisons. In Proceedings of the 25th ACM SIGKDD
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H Chi, et al · 2019
Earlier work this paper cites.
Controlling polarization in personalization: An algorithmic framework. In Proceedings of the conference on fairness, accountability, and transparency . 160–169
L Elisa Celis, Sayash Kapoor, Farnood Salehi, and Nisheeth Vishnoi. 2019 · 2019
Earlier work this paper cites.
Generate natural language explanations for recommendation
Hanxiong Chen, Xu Chen, Shaoyun Shi, and Yongfeng Zhang. 2019a · 2019
Earlier work this paper cites.
Fairness and discrimination in recommendation and retrieval. In Proceedings of the 13th ACM Conference on Recommender Systems . 576–577
Michael D Ekstrand, Robin Burke, and Fernando Diaz. 2019 · 2019
Earlier work this paper cites.
Explainable recommendation through attentive multi-view learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 3622–3629
Jingyue Gao, Xiting Wang, Yasha Wang, and Xing Xie. 2019 · 2019
Cited alongside, same era.
How Fair Can We Go: Detecting the Boundaries of Fairness Optimization in Information Retrieval. In Proceedings of ICTIR ’19 . ACM, New York, NY, USA, 229–236
Ruoyuan Gao and Chirag Shah. 2019 · 2019
Cited alongside, same era.
Maximizing marginal utility per dollar for economic recommendation. In The World Wide Web Conference . 2757–2763
Yingqiang Ge, Shuyuan Xu, Shuchang Liu, Shijie Geng, Zuohui Fu, and Yongfeng Zhang. 2019 · 2019
Cited alongside, same era.
Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search. In Proceedings of KDD . ACM, 2221–2231
Sahin Cem Geyik, Stuart Ambler, and Krishnaram Kenthapadi. 2019 · 2019
Cited alongside, same era.
The ethical algorithm: The science of socially aware algorithm design
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, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracing
Mohammad Mahdi Kamani, Rana Forsati, James Z Wang, and Mehrdad Mahdavi. 2021 · 2021
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Explainable Recommendation with Comparative Constraints on Product Aspects. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining . 967–975
Trung-Hoang Le and Hady W Lauw. 2021 · 2021
Later among the works it cites.
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. 2021a · 2021
Later among the works it cites.
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Michael Kearns and Aaron Roth. 2019 · 2019
Cited alongside, same era.
Reinforcement knowledge graph reasoning for explainable recommendation. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval . 285–294
Yikun Xian, Zuohui Fu, Shan Muthukrishnan, Gerard De Melo, and Yongfeng Zhang. 2019 · 2019
Cited alongside, same era.
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 4085–4094
Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola. 2019 · 2019
Cited alongside, same era.
Explainability for fair machine learning
Tom Begley, Tobias Schwedes, Christopher Frye, and Ilya Feige. 2020 · 2020
Cited alongside, same era.
Try this instead: Personalized and interpretable substitute recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 891–900
Tong Chen, Hongzhi Yin, Guanhua Ye, Zi Huang, Yang Wang, and Meng Wang. 2020 · 2020
Cited alongside, same era.
Fairness-aware explainable recommendation over knowledge graphs. In Proceedings of the 43rd SIGIR . 69–78
Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, et al · 2020
Cited alongside, same era.
PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems
Azin Ghazimatin, Oana Balalau, Rishiraj Saha Roy, and Gerhard Weikum. 2020 · 2020
Cited alongside, same era.
Generate neural template explanations for recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 755–764
Lei Li, Yongfeng Zhang, and Li Chen. 2020 · 2020
Cited alongside, same era.
Variation Control and Evaluation for Generative Slate Recommendations. In Proceedings of the Web Conference 2021 . 436–448
Shuchang Liu, Fei Sun, Yingqiang Ge, Changhua Pei, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Explaining algorithmic fairness through fairness-aware causal path decomposition. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 1287–1297
Weishen Pan, Sen Cui, Jiang Bian, Changshui Zhang, and Fei Wang. 2021 · 2021
Later among the works it cites.
Counterfactual explainable recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 1784–1793
Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Counterfactual Explanations for Neural Recommenders
Khanh Hiep Tran, Azin Ghazimatin, and Rishiraj Saha Roy. 2021 · 2021
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Ex3: Explainable attribute-aware item-set recommendations. In Fifteenth ACM Conference on Recommender Systems . 484–494
Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, Shan Muthukrishnan, and Yongfeng Zhang. 2021 · 2021
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Causal Collaborative Filtering
Shuyuan Xu, Yingqiang Ge, Yunqi Li, Zuohui Fu, Xu Chen, and Yongfeng Zhang. 2021a · 2021
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Learning Causal Explanations for Recommendation
Shuyuan Xu, Yunqi Li, Shuchang Liu, Zuohui Fu, Yingqiang Ge, Xu Chen, and Yongfeng Zhang. 2021b · 2021
Later among the works it cites.
Maximizing Marginal Fairness for Dynamic Learning to Rank. In Proceedings of the Web Conference 2021 . 137–145
Tao Yang and Qingyao Ai. 2021 · 2021
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Faithfully Explainable Recommendation via Neural Logic Reasoning. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies . 3083–3090
Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Graph Collaborative Reasoning. In Proceedings of the 15th WSDM
Hanxiong Chen, Li Yunqi, Shi Shaoyun, Shuchang Liu, He Zhu, and Yongfeng Zhang. 2022 · 2022
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Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning
Yingqiang Ge, Xiaoting Zhao, Lucia Yu, Saurabh Paul, Diane Hu, Chu-Cheng Hsieh, and Yongfeng Zhang. 2022 · 2022
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Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
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Personalized Prompt Learning for Explainable Recommendation
Lei Li, Yongfeng Zhang, and Li Chen. 2022b · 2022
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AutoLossGen: Automatic Loss Function Generation for Recommender Systems
Zelong Li, Jianchao Ji, Yingqiang Ge, and Yongfeng Zhang. 2022a · 2022
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Improving Personalized Explanation Generation through Visualization. In ACL
Yingqiang Ge Lei Li Gerard de Melo Yongfeng Zhang Shijie Geng, Zuohui Fu. 2022 · 2022
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Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning
Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, and Yongfeng Zhang. 2022 · 2022
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Joint Multisided Exposure Fairness for Recommendation
Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, and Xue Liu. 2022 · 2022
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