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Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news.
Non-null ranking models. i
Mallows, C. L. (1957) · 1957
Earlier work this paper cites.
Distance based ranking models
Fligner, M. A. and Verducci, J. S. (1986) · 1986
Earlier work this paper cites.
Optimal ranking and choice from pairwise comparisons
Young, H. P. (1986) · 1986
Earlier work this paper cites.
Multistage ranking models
Fligner, M. A. and Verducci, J. S. (1988) · 1988
Earlier work this paper cites.
Tutorial on large deviations for the binomial distribution
Arratia, R. and Gordon, L. (1989) · 1989
Earlier work this paper cites.
Probability models on rankings
Critchlow, D. E., Fligner, M. A., and Verducci, J. S. (1991) · 1991
Earlier work this paper cites.
Rank aggregation methods for the web
Dwork, C., Kumar, R., Naor, M., and Sivakumar, D. (2001) · 2001
Earlier work this paper cites.
Generalized random utility model
Walker, J. and Ben-Akiva, M. (2002) · 2002
Earlier work this paper cites.
The boosting approach to machine learning: An overview
Schapire, R. E. (2003) · 2003
Earlier work this paper cites.
On ψ \psi -learning
Shen, X., Tseng, G. C., Zhang, X., and Wong, W. H. (2003) · 2003
Earlier work this paper cites.
Statistical behavior and consistency of classification methods based on convex risk minimization
Zhang, T. (2004) · 2004
Earlier work this paper cites.
Kernel logistic regression and the import vector machine
Zhu, J. and Hastie, T. (2005) · 2005
Earlier work this paper cites.
Convexity, classification, and risk bounds
Bartlett, P. L., Jordan, M. I., and McAuliffe, J. D. (2006) · 2006
Earlier work this paper cites.
Differential privacy
Dwork, C. (2006) · 2006
Earlier work this paper cites.
Our data, ourselves: Privacy via distributed noise generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., and Naor, M. (2006) · 2006
Earlier work this paper cites.
Supervised rank aggregation
Liu, Y.-T., Liu, T.-Y., Qin, T., Ma, Z.-M., and Li, H. (2007) · 2007
Earlier work this paper cites.
Mechanism design via differential privacy
McSherry, F. and Talwar, K. (2007) · 2007
Earlier work this paper cites.
The linear combination, product and ratio of laplace random variables
Nadarajah, S. (2007) · 2007
Earlier work this paper cites.
Tractable search for learning exponential models of rankings
Mandhani, B. and Meila, M. (2009) · 2009
Earlier work this paper cites.
An exponential model for infinite rankings
Meilă, M. and Bao, L. (2010) · 2010
Earlier work this paper cites.
Probabilistic inference and differential privacy
Williams, O. and McSherry, F. (2010) · 2010
Earlier work this paper cites.
Collaborative ranking
Balakrishnan, S. and Chopra, S. (2012) · 2012
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Estimation in discrete parameter models
Choirat, C. and Seri, R. (2012) · 2012
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Third-party web tracking: Policy and technology
Mayer, J. R. and Mitchell, J. C. (2012) · 2012
Cited alongside, same era.
Bpr: Bayesian personalized ranking from implicit feedback
Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2012) · 2012
Cited alongside, same era.
Privacy in recommender systems
Jeckmans, A. J., Beye, M., Erkin, Z., Hartel, P., Lagendijk, R. L., and Tang, Q. (2013) · 2013
Cited alongside, same era.
Learning to rank for recommender systems
Karatzoglou, A., Baltrunas, L., and Shi, Y. (2013) · 2013
Cited alongside, same era.
Privacy loss in apple’s implementation of differential privacy on macos 10.12
Tang, J., Korolova, A., Bai, X., Wang, X., and Wang, X. (2017) · 2017
Later among the works it cites.
Locally differentially private protocols for frequency estimation
Wang, T., Blocki, J., Li, N., and Jha, S. (2017) · 2017
Later among the works it cites.
The right complexity measure in locally private estimation: It is not the fisher information
Duchi, J. C. and Ruan, F. (2018) · 2018
Later among the works it cites.
Truth inference on sparse crowdsourcing data with local differential privacy
Sun, H., Dong, B., Wang, H., Yu, T., and Qin, Z. (2018) · 2018
Later among the works it cites.
Mallows ranking models: maximum likelihood estimate and regeneration
Tang, W. (2019) · 2019
Later among the works it cites.
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Cited alongside, same era.
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