Fetching the paper…
Reading the bibliography…
Traditional recommender systems aim to estimate a user's rating to an item based on observed ratings from the population.
1902
Earlier work this paper cites.
D. B. Rubin, “Bayesianly justifiable and relevant frequency calculations for the applies statistician,” Ann. Stat. , pp. 1151–1172, 1984
1984
Earlier work this paper cites.
P. W. Holland, “Statistics and causal inference,” J. Amer. Statist. Assoc. , vol. 81, no. 396, pp. 945–960, 1986
1986
Earlier work this paper cites.
D. B. Rubin, “Formal model of statistical inference for causal effects,” J. Stat. Plan. Inference , vol. 25, no. 3, pp. 279–292, 1990
1990
Earlier work this paper cites.
R. A. Johnson, D. W. Wichern et al. , “Multivariate linear regression models,” in Applied Multivariate Statistical Analysis , 2002, ch. 7, pp. 360–417
2002
Earlier work this paper cites.
2003
Earlier work this paper cites.
K. Imai and D. A. Van Dyk, “Causal inference with general treatment regimes: Generalizing the propensity score,” J. Amer. Statist. Assoc. , vol. 99, no. 467, pp. 854–866, 2004
2004
Earlier work this paper cites.
Y. Koren, “Factorization meets the neighborhood: A multifaceted collaborative filtering model,” in Proc. 14th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining , 2008, pp. 426–434
2008
Earlier work this paper cites.
Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” in Proc. 8th IEEE Int. Conf. Data Mining , 2008, pp. 263–272
2008
Earlier work this paper cites.
A. Gelman, “Resolving disputes between J. Pearl and D. Rubin on causal inference,” Statistical Modeling, Causal Inference, and Social Science , 2009
2009
Earlier work this paper cites.
B. M. Marlin and R. S. Zemel, “Collaborative prediction and ranking with non-random missing data,” in Proc. ACM Conf. Recommender Syst. , 2009, pp. 5–12
2009
Earlier work this paper cites.
H. Steck, “Training and testing of recommender systems on data missing not at random,” in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining , 2010, pp. 713–722
2010
Earlier work this paper cites.
H. Steck, “Item popularity and recommendation accuracy,” in Proc. ACM Conf. Recommender Syst. , 2011, pp. 125–132
2011
Earlier work this paper cites.
B. Pradel, N. Usunier, and P. Gallinari, “Ranking with non-random missing ratings: influence of popularity and positivity on evaluation metrics,” in Proc. ACM Conf. Recommender Syst. , 2012, pp. 147–154
2012
Earlier work this paper cites.
D. Bouneffouf, A. Bouzeghoub, and A. L. Gançarski, “A contextual-bandit algorithm for mobile context-aware recommender system,” in Proc. Int. Conf. Neural Inf. Process. Syst. . Springer, 2012, pp. 324–331
2012
Earlier work this paper cites.
H. Steck, “Evaluation of recommendations: Rating-prediction and ranking,” in Proc. ACM Conf. Recommender Syst. , 2013, pp. 213–220
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” Proc. Int. Conf. Learn. Representations , 2013
2013
Cited alongside, same era.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” Proc. Int. Conf. Learn. Representations , 2014
2014
Cited alongside, same era.
G. W. Imbens and D. B. Rubin, Causal Inference in Statistics, Social, and Biomedical Sciences . Cambridge University Press, 2015
2015
Cited alongside, same era.
D. Liang, M. Zhan, and D. P. Ellis, “Content-aware collaborative music recommendation using pre-trained neural networks.” in Proc. ISMIR , 2015, pp. 295–301
2015
Cited alongside, same era.
T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, and T. Joachims, “Recommendations as treatments: Debiasing learning and evaluation,” in Proc. Int. Conf. Mach. Learn. , 2016, pp. 1670–1679
2016
Y. Wang, D. Liang, L. Charlin, and D. M. Blei, “Causal inference for recommender systems,” in Proc. ACM Conf. Recommender Syst. , 2020, pp. 426–431
2020
Later among the works it cites.
H. Zou, P. Cui, B. Li, Z. Shen, J. Ma, H. Yang, and Y. He, “Counterfactual prediction for bundle treatment,” Proc. Int. Conf. Neural Inf. Process. Syst. , vol. 33, 2020
2020
Later among the works it cites.
N. Pawlowski, D. Coelho de Castro, and B. Glocker, “Deep structural causal models for tractable counterfactual inference,” Proc. Int. Conf. Neural Inf. Process. Syst. , vol. 33, 2020
2020
Later among the works it cites.
Y. Luo, J. Peng, and J. Ma, “When causal inference meets deep learning,” Nat. Mach. Intell. , vol. 2, no. 8, pp. 426–427, 2020
2020
Later among the works it cites.
M. Sato, S. Takemori, J. Singh, and T. Ohkuma, “Unbiased learning for the causal effect of recommendation,” in Proc. ACM Conf. Recommender Syst. , 2020, pp. 378–387
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
J. Pearl, M. Glymour, and N. P. Jewell, Causal Inference in Statistics: A Primer . John Wiley & Sons, 2016
2016
Cited alongside, same era.
T. Joachims, A. Swaminathan, and T. Schnabel, “Unbiased learning-to-rank with biased feedback,” in Proc. ACM Int. Conf. Web Search Data Mining , 2017, pp. 781–789
2017
Cited alongside, same era.
C. Louizos, U. Shalit, J. Mooij, D. Sontag, R. Zemel, and M. Welling, “Causal effect inference with deep latent-variable models,” Proc. Int. Conf. Neural Inf. Process. Syst. , 2017
2017
Cited alongside, same era.
D. Liang, R. G. Krishnan, M. D. Hoffman, and T. Jebara, “Variational autoencoders for collaborative filtering,” in Proc. Int. Conf. World Wide Web Conf. , 2018, pp. 689–698
2018
Cited alongside, same era.
S. Bonner and F. Vasile, “Causal embeddings for recommendation,” in Proc. ACM Conf. Recommender Syst. , 2018, pp. 104–112
2018
Cited alongside, same era.
V. Rakesh, R. Guo, R. Moraffah, N. Agarwal, and H. Liu, “Linked causal variational autoencoder for inferring paired spillover effects,” in Proc. ACM Int. Conf. Inf. Knowl. Manage. , 2018, pp. 1679–1682
2018
Cited alongside, same era.
Y. Wang and D. M. Blei, “The blessings of multiple causes,” J. Amer. Statist. Assoc. , vol. 114, no. 528, pp. 1574–1596, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
J. Chang, C. Gao, X. He, D. Jin, and Y. Li, “Bundle recommendation and generation with graph neural networks,” IEEE Trans. Knowl. Data Eng. , to be published, doi: 10.1109/TKDE.2021.3114586
2021
Later among the works it cites.
J. Ma, R. Guo, A. Zhang, and J. Li, “Multi-cause effect estimation with disentangled confounder representation,” in Proc. Int. Joint Conf. Artif. Intell. , 2021
2021
Later among the works it cites.
J. Zhang, X. Chen, and W. X. Zhao, “Causally attentive collaborative filtering,” in Proc. ACM Int. Conf. Inf. Knowl. Manage. , 2021, pp. 3622–3626
2021
Later among the works it cites.
J. Tan, S. Xu, Y. Ge, Y. Li, X. Chen, and Y. Zhang, “Counterfactual explainable recommendation,” in Proc. ACM Int. Conf. Inf. Knowl. Manage. , 2021, pp. 1784–1793
2021
Later among the works it cites.
Y. Zhang, F. Feng, X. He, T. Wei, C. Song, G. Ling, and Y. Zhang, “Causal intervention for leveraging popularity bias in recommendation,” in Proc. 39th Int. ACM SIGIR Conf. Res. Develop. Inf. Retrieval , 2021, p. 11–20
2021
Later among the works it cites.
Y. Zheng, C. Gao, X. Li, X. He, Y. Li, and D. Jin, “Disentangling user interest and conformity for recommendation with causal embedding,” in Proc. Int. Conf. World Wide Web Conf. , 2021, pp. 2980–2991
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Schölkopf, F. Locatello, S. Bauer, N. R. Ke, N. Kalchbrenner, A. Goyal, and Y. Bengio, “Toward causal representation learning,” Proc. IEEE , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
R. Salakhutdinov and N. Srebro, “Collaborative filtering in a non-uniform world: Learning with the weighted trace norm,” in Proc. Int. Conf. Neural Inf. Process. Syst. , 2010, pp. 2056–2064
2064
Closest in time.