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Recommendation fairness has attracted great attention recently.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
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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
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Generative adversarial nets. In Proceedings of NIPS
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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Correcting Popularity Bias by Enhancing Recommendation Neutrality.. In RecSys Posters
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh, and Jun Sakuma. 2014 · 2014
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Léon Bottou. 2017 · 2017
Earlier work this paper cites.
Multisided fairness for recommendation
Robin Burke. 2017 · 2017
Earlier work this paper cites.
When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the Eleventh ACM Conference on Recommender Systems . 306–310
Dietmar Jannach and Malte Ludewig. 2017 · 2017
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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
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Self-attentive sequential recommendation. In Proceedings of ICDM
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
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Deep interest network for click-through rate prediction. In Proceedings of KDD
Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018 · 2018
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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.
Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning . PMLR, 2790–2799
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019 · 2019
Cited alongside, same era.
A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation. In Proceedings of the 13th ACM Conference on recommender systems . 20–28
Xiao Lin, Hongjie Chen, Changhua Pei, Fei Sun, Xuanji Xiao, Hanxiao Sun, Yongfeng Zhang, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Personalized fairness-aware re-ranking for microlending. In Proceedings of the 13th ACM Conference on Recommender Systems . 467–471
Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, and Shengyu Zhang. 2019 · 2019
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning. In Proceedings of EMNLP
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Later among the works it cites.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Later among the works it cites.
Towards personalized fairness based on causal notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1054–1063
Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2021 · 2021
Later among the works it cites.
Fairness in rankings and recommendations: An overview
Evaggelia Pitoura, Kostas Stefanidis, and Georgia Koutrika. 2021 · 2021
Later among the works it cites.
Learning fair representations for recommendation: A graph-based perspective. In Proceedings of the Web Conference 2021 . 2198–2208
Le Wu, Lei Chen, Pengyang Shao, Richang Hong, Xiting Wang, and Meng Wang. 2021a · 2021
Later among the works it cites.
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Autoint: Automatic feature interaction learning via self-attentive neural networks. In Proceedings of CIKM
Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, and Jian Tang. 2019 · 2019
Cited alongside, same era.
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. In Proceedings of CIKM
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Cited alongside, same era.
S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization. In CIKM
Kun Zhou, Hui Wang, Wayne Xin Zhao, Yutao Zhu, Sirui Wang, Fuzheng Zhang, Zhongyuan Wang, and Ji-Rong Wen. 2020 · 2020
Cited alongside, same era.
Modeling users’ behavior sequences with hierarchical explainable network for cross-domain fraud detection. In Proceedings of The Web Conference 2020
Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, and Qing He. 2020 · 2020
Cited alongside, same era.
Adversarial Feature Translation for Multi-domain Recommendation. In Proceedings of KDD
Xiaobo Hao, Yudan Liu, Ruobing Xie, Kaikai Ge, Linyao Tang, Xu Zhang, and Leyu Lin. 2021 · 2021
Cited alongside, same era.
Fairrec: fairness-aware news recommendation with decomposed adversarial learning. AAAI
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, and Xing Xie. 2021b
Cited in the paper.
Deep Feedback Network for Recommendation. In Proceedings of IJCAI
Ruobing Xie, Cheng Ling, Yalong Wang, Rui Wang, Feng Xia, and Leyu Lin. 2020a
Cited in the paper.
UPRec: User-Aware Pre-training for Recommender Systems
Chaojun Xiao, Ruobing Xie, Yuan Yao, Zhiyuan Liu, Maosong Sun, Xu Zhang, and Leyu Lin. 2021 · 2021
Later among the works it cites.
Contrastive Cross-domain Recommendation in Matching
Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, and Leyu Lin. 2021a · 2021
Later among the works it cites.
Knowledge transfer via pre-training for recommendation: A review and prospect
Zheni Zeng, Chaojun Xiao, Yuan Yao, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun. 2021 · 2021
Later among the works it cites.
Learning to warm up cold item embeddings for cold-start recommendation with meta scaling and shifting networks. In Proceedings of SIGIR . 1167–1176
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin, and Juan Cao. 2021 · 2021
Later among the works it cites.
Multi-view Multi-behavior Contrastive Learning in Recommendation. In Proceedings of DASFAA
Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, and Qing He. 2022 · 2022
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