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Fairness em RecSys - Coggle Diagram
Fairness em RecSys
em RecSys
Steck, H. (2018).
Calibrated Recommendations.
https://doi.org/10.1145/3240323
Leonhardt, J., Anand, A., & Khosla, M. (2018).
User Fairness in Recommender Systems
. In The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018 (pp. 101–102). Association for Computing Machinery, Inc.
https://doi.org/10.1145/3184558.3186949
Farnadi, G., Kouki, P., Thompson, S. K., Srinivasan, S., & Getoor, L. (2018).
A Fairness-aware Hybrid Recommender System
. Retrieved from
http://arxiv.org/abs/1809.09030
Sonboli, N., & Burke, R. (2019).
Localized fairness in recommender systems
. In ACM UMAP 2019 Adjunct - Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (pp. 295–300). New York, New York, USA: Association for Computing Machinery, Inc.
https://doi.org/10.1145/3314183.3323845
Liu, W., Liu, F., Tang, R., Liao, B., Chen, G., & Heng, P. A. (2020).
Balancing Between Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12084 LNAI, pp. 155–167). Springer.
https://doi.org/10.1007/978-3-030-47426-3_13
Advances in Knowledge Discovery and Data Mining
https://pakdd2020.org/download/conference_paper_slides/main-259.pdf
Datasets: MoviesLens, Kiva.org
Beutel, A., Chen, J., Doshi, T., Qian, H., Wei, L., Wu, Y., … Goodrow, C. (2019).
Fairness in Recommendation Ranking through Pairwise Comparisons
.
https://doi.org/10.1145/3292500.3330745
Deldjoo, Y., Anelli, V. W., Zamani, H., Bellogín, A., & Di Noia, T. (2019).
Recommender Systems Fairness Evaluation via Generalized Cross Entropy
*.
Yao, S., & Huang, B. (n.d.).
Beyond Parity: Fairness Objectives for Collaborative Filtering.
(2017)
Balanced Neighborhoods for Multi-sided Fairness
in Recommendation
(2018)
Borges, R., & Stefanidis, K. (n.d.).
Enhancing Long Term Fairness in Recommendations with Variational Autoencoders.
**
(2019)
https://people.uta.fi/~kostas.stefanidis/docs/medes19.pdf
https://trepo.tuni.fi/bitstream/handle/10024/121479/ImtiazWaleed.pdf?sequence=2&isAllowed=y
Fairness in Rankings and Recommenders
(2018)
https://pdfs.semanticscholar.org/fc7f/723cc1e4a390dbf286a38d642b8b07bdb22f.pdf
On the Need for Fairness in Financial
Recommendation Engines
(2020)
http://people.cs.vt.edu/~bhuang/papers/yao-finance18.pdf
Fairness em Machine Learning
Gajane, P., & Pechenizkiy, M. (2018).
On Formalizing Fairness in Prediction with Machine Learning
.
Bilal Zafar, M., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (n.d.).
Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment.
https://doi.org/10.1145/3038912.3052660
(2017)
D’amour, A., Srinivasan, H., Atwood, J., Research, G., Baljekar, P., Sculley, D., & Halpern, Y. (2020).
Fairness Is Not Static: Deeper Understanding of Long Term Fairness via Simulation Studies
1 INTRO: BEYOND STATIC FAIRNESS. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. New York, NY, USA: ACM. Retrieved from
https://doi.org/10.1145/3351095.3372878
Metevier, B., Giguere, S., Brockman, S., Kobren, A., Brun, Y., Brunskill, E., & Thomas, P. S. (n.d.).
Offline Contextual Bandits with High Probability Fairness Guarantees.
(2019)
Joseph, M., Kearns, M., Morgenstern, J., & Roth, A. (2016).
Fairness in Learning: Classic and Contextual Bandits
*.
Chen, Y., Cuellar, A., Luo, H., Modi, J., Nemlekar, H., & Nikolaidis, S. (n.d.).
Fair Contextual Multi-Armed Bandits: Theory and Experiments
. (2019)
Binns, R. (2018).
Fairness in Machine Learning: Lessons from Political Philosophy.
Proceedings of Machine Learning Research (Vol. 81).
Pedreschi, D., Ruggieri, S., & Turini, F. (2008).
Discrimination-aware Data Mining
.
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (n.d.).
A Survey on Bias and Fairness in Machine Learning
. Retrieved from
https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
(2019)
em Marketplace
Mehrotra, R., Mcinerney, J., Bouchard, H., Lalmas, M., & Diaz, F. (n.d.).
Towards a Fair Marketplace : Counterfactual Evaluation of the trade-off between Relevance , Fairness & Satisfaction in Recommendation Systems.
2018
Burke, R. (2017).
Multisided Fairness for Recommendation.
Retrieved from www.etsy.com
Abdollahpouri, H., & Burke, R. (n.d.).
Multi-stakeholder Recommendation and its Connection to Multi-sided Fairness
**
. Retrieved from
https://eng.uber.com/uber-eats-recommending-marketplace/
Facets of Fairness in Search and Recommendation
(2020) -
https://arxiv.org/pdf/2008.01194.pdf
A Framework for Fairness in Two-Sided
Marketplaces
(2020) -
https://arxiv.org/pdf/2006.12756.pdf
Patro, G. K., Biswas, A., Ganguly, N., Gummadi, K. P., & Chakraborty, A. (2020).
FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms.
In The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020 (pp. 1194–1204). New York, NY, USA: Association for Computing Machinery, Inc.
https://doi.org/10.1145/3366423.3380196
Dataset
: Google Local Ratings Dataset, Last.fm Dataset
Patro, G. K., Chakraborty, A., Ganguly, N., & PGummadi, K. (n.d.). I
ncremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations
. Retrieved from www.aaai.org
dataset
: Amazon products and Google Local datasets
Links
A Tutorial on Fairness in Machine Learning
https://towardsdatascience.com/a-tutorial-on-fairness-in-machine-learning-3ff8ba1040cb
Fairness in Recommender Systems
http://www.ec.tuwien.ac.at/~dimitris/research/recsys-fairness.html
Fairness and machine learning
Solon Barocas, Moritz Hardt, Arvind Narayanan
https://fairmlbook.org/
Dataset
https://cseweb.ucsd.edu/~jmcauley/datasets.html
Last.fm Dataset
http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html
Fairness In Ranked
Yang, K., & Stoyanovich, J. (2017).
Measuring Fairness in Ranked Outputs
, (6). 2017
https://doi.org/10.1145/3085504.3085526
Singh, A., & Joachims, T. (2018).
Fairness of Exposure in Rankings
, 10.
https://doi.org/10.1145/3219819.3220088
Chen, Y., Cuellar, A., Luo, H., Modi, J., Nemlekar, H., & Nikolaidis, S. (2020).
The Fair Contextual Multi-Armed Bandit.
Retrieved from www.ifaamas.org
