How Fair is Your Diffusion Recommender Model?

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Generative adversarial networks

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Denoising Diffusion Probabilistic Models. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , Hugo Larochelle, Marc’Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin (Eds.)

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RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback. In WSDM ’20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020 , James Caverlee, Xia (Ben) Hu, Mounia Lalmas, and Wei Wang (Eds.). ACM, 528–536

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Structured Denoising Diffusion Models in Discrete State-Spaces. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual , Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 17981–17993

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Pair-wise ranking based preference learning for points-of-interest recommendation

Qigang Liu, Lifeng Mu, Vijayan Sugumaran, Chongren Wang, and Dongmei Han. 2021 · 2021

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Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, and Claudio Pomo. 2022 · 2022

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Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations. In Advances in Information Retrieval - 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10-14, 2022, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 13185) , Matthias Hagen, Suzan Verberne, Craig Macdonald, Christin Seifert, Krisztian Balog, Kjetil Nørvåg, and Vinay Setty (Eds.). Springer, 552–566

Ludovico Boratto, Gianni Fenu, Mirko Marras, and Giacomo Medda. 2022 · 2022

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Practical perspectives of consumer fairness in recommendation

Ludovico Boratto, Gianni Fenu, Mirko Marras, and Giacomo Medda. 2023b · 2022

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Equivariant Diffusion for Molecule Generation in 3D. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162) , Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato (Eds.). PMLR, 8867–8887

Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling. 2022 · 2022

Cited alongside, same era.

Diffusion-LM Improves Controllable Text Generation. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 , Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (Eds.)

Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B. Hashimoto. 2022 · 2022

Cited alongside, same era.

A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems

Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, and Robin Burke. 2022 · 2022

Cited alongside, same era.

XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation

Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2024 · 2023

Later among the works it cites.

Fair Augmentation for Graph Collaborative Filtering. In Proceedings of the 18th ACM Conference on Recommender Systems, RecSys 2024, Bari, Italy, October 14-18, 2024 , Tommaso Di Noia, Pasquale Lops, Thorsten Joachims, Katrien Verbert, Pablo Castells, Zhenhua Dong, and Ben London (Eds.). ACM, 158–168

Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, and Giacomo Medda. 2024a · 2024

Closest in time.

Robustness in Fairness Against Edge-Level Perturbations in GNN-Based Recommendation. In Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24-28, 2024, Proceedings, Part III (Lecture Notes in Computer Science, Vol. 14610) , Nazli Goharian, Nicola Tonellotto, Yulan He, Aldo Lipani, Graham McDonald, Craig Macdonald, and Iadh Ounis (Eds.). Springer, 38–55

Ludovico Boratto, Francesco Fabbri, Gianni Fenu, Mirko Marras, and Giacomo Medda. 2024b · 2024

Closest in time.