Fetching the paper…
Reading the bibliography…
Latent factor models for recommender systems represent users and items as low dimensional vectors.
We know what you want to buy: a demographic-based system for product recommendation on microblogs
Zhao, X. W., Guo, Y., He, Y., Jiang, H., Wu, Y., and Li, X. (2014) · 1944
Earlier work this paper cites.
A framework for collaborative, content-based and demographic filtering
Pazzani, M. J. (1999) · 1999
Earlier work this paper cites.
Movielens unplugged: experiences with an occasionally connected recommender system
Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., and Riedl, J. (2003) · 2003
Earlier work this paper cites.
The netflix prize
Bennett, J., Lanning, S., et al. (2007) · 2007
Earlier work this paper cites.
Extraction and integration of movielens and imdb data
Peralta, V. (2007) · 2007
Earlier work this paper cites.
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Koren, Y. (2008) · 2008
Earlier work this paper cites.
Robust de-anonymization of large sparse datasets
Narayanan, A. and Shmatikov, V. (2008) · 2008
Earlier work this paper cites.
Differentially private recommender systems: Building privacy into the netflix prize contenders
McSherry, F. and Mironov, I. (2009) · 2009
Earlier work this paper cites.
Bpr: Bayesian personalized ranking from implicit feedback
Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. (2009) · 2009
Earlier work this paper cites.
Social comparisons and contributions to online communities: A field experiment on movielens
Chen, Y., Harper, F. M., Konstan, J., and Li, S. X. (2010) · 2010
Cited alongside, same era.
Attribute selection-based recommendation framework for short-head user group: An empirical study by movielens and imdb
Jung, J. J. (2012) · 2012
Cited alongside, same era.
Blurme: Inferring and obfuscating user gender based on ratings
Weinsberg, U., Bhagat, S., Ioannidis, S., and Taft, N. (2012) · 2012
Cited alongside, same era.
Privacy-preserving matrix factorization
Nikolaenko, V., Ioannidis, S., Weinsberg, U., Joye, M., Taft, N., and Boneh, D. (2013) · 2013
Cited alongside, same era.
Learning fair representations
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. (2013) · 2013
Cited alongside, same era.
Privacy-preserving personalized recommendation: An instance-based approach via differential privacy
Deep neural networks for youtube recommendations
Covington, P., Adams, J., and Sargin, E. (2016) · 2016
Later among the works it cites.
A differential privacy framework for matrix factorization recommender systems
Friedman, A., Berkovsky, S., and Kaafar, M. A. (2016) · 2016
Later among the works it cites.
The movielens datasets: History and context
Harper, F. M. and Konstan, J. A. (2016) · 2016
Later among the works it cites.
Data decisions and theoretical implications when adversarially learning fair representations
Beutel, A., Chen, J., Zhao, Z., and Chi, E. H. (2017) · 2017
Later among the works it cites.
Controllable invariance through adversarial feature learning
Xie, Q., Dai, Z., Du, Y., Hovy, E., and Neubig, G. (2017) · 2017
Later among the works it cites.
Adversarial removal of demographic attributes from text data
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Shen, Y. and Jin, H. (2014) · 2014
Cited alongside, same era.
Context-aware recommender systems
Adomavicius, G. and Tuzhilin, A. (2015) · 2015
Cited alongside, same era.
Applying differential privacy to matrix factorization
Berlioz, A., Friedman, A., Kaafar, M. A., Boreli, R., and Berkovsky, S. (2015) · 2015
Cited alongside, same era.
Fast differentially private matrix factorization
Liu, Z., Wang, Y.-X., and Smola, A. (2015) · 2015
Cited alongside, same era.
Elazar, Y. and Goldberg, Y. (2018) · 2018
Closest in time.
Mitigating unwanted biases with adversarial learning
Zhang, B. H., Lemoine, B., and Mitchell, M. (2018) · 2018
Closest in time.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016) · 2030
Closest in time.