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Numerical evaluations with comparisons to baselines play a central role when judging research in recommender systems.
Netflix update: Try this at home
Funk, S · 2006
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The bellkor solution to the netflix prize, 2007
Bell, R. M., Koren, Y., and Volinsky, C · 2007
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The netflix prize
Bennett, J., and Lanning, S · 2007
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On the gravity recommendation system
Gabor Takacs, Istvan Pilaszy, B. N., and Tikk, D · 2007
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Methods for large scale svd with missing values
Kurucz, M., Benczúr, A. A., and Csalogány, K · 2007
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Variational bayesian approach to movie rating prediction
Lim, Y. J., and Te, Y. W · 2007
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Improving regularized singular value decomposition for collaborative filtering
Paterek, A · 2007
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Restricted boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A., and Hinton, G · 2007
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Factorization meets the neighborhood: A multifaceted collaborative filtering model
Koren, Y · 2008
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Bayesian probabilistic matrix factorization using markov chain monte carlo
Salakhutdinov, R., and Mnih, A · 2008
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Large-scale parallel collaborative filtering for the netflix prize
Zhou, Y., Wilkinson, D., Schreiber, R., and Pan, R · 2008
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The bellkor solution to the netflix grand prize, 2009
Koren, Y · 2009
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Collaborative filtering with temporal dynamics
Koren, Y · 2009
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Matrix factorization techniques for recommender systems
Koren, Y., Bell, R., and Volinsky, C · 2009
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The pragmatic theory solution to the netflix grand prize, 2009
Piotte, M., and Chabbert, M · 2009
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The bigchaos solution to the netflix grand prize, 2009
Töscher, A., and Jahrer, M · 2009
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An accelerated proximal gradient algorithm for nuclear norm regularized least squares problems
chuan Toh, K., and Yun, S · 2010
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Fast ALS-based matrix factorization for explicit and implicit feedback datasets
Pilászy, I., Zibriczky, D., and Tikk, D · 2010
Cited alongside, same era.
Bayesian factorization machines
Freudenthaler, C., Schmidt-Thieme, L., and Rendle, S · 2011
Cited alongside, same era.
Wemarec: Accurate and scalable recommendation through weighted and ensemble matrix approximation
Chen, C., Li, D., Zhao, Y., Lv, Q., and Shang, L · 2015
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The movielens datasets: History and context
Harper, F. M., and Konstan, J. A · 2015
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Autorec: Autoencoders meet collaborative filtering
Sedhain, S., Menon, A. K., Sanner, S., and Xie, L · 2015
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Mpma: Mixture probabilistic matrix approximation for collaborative filtering
Chen, C., Li, D., Lv, Q., Yan, J., Chu, S. M., and Shang, L · 2016
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Low-rank matrix approximation with stability
Li, D., Chen, C., Lv, Q., Yan, J., Shang, L., and Chu, S. M · 2016
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Divide-and-conquer matrix factorization
Mackey, L., Talwalkar, A., and Jordan, M. I · 2011
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Svdfeature: A toolkit for feature-based collaborative filtering
Chen, T., Zhang, W., Lu, Q., Chen, K., Zheng, Z., and Yu, Y · 2012
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Factorization machines with libfm
Rendle, S · 2012
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Local low-rank matrix approximation
Lee, J., Kim, S., Lebanon, G., and Singer, Y · 2013
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Scaling factorization machines to relational data
Rendle, S · 2013
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Recommendation by mining multiple user behaviors with group sparsity
Yuan, T., Cheng, J., Zhang, X., Qiu, S., and Lu, H · 2014
Cited alongside, same era.
Strub, F., Mary, J., and Gaudel, R · 2016
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A neural autoregressive approach to collaborative filtering
Zheng, Y., Tang, B., Ding, W., and Zhou, H · 2016
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Gloma: Embedding global information in local matrix approximation models for collaborative filtering
Chen, C., Li, D., Lv, Q., Yan, J., Shang, L., and Chu, S · 2017
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Mixture-rank matrix approximation for collaborative filtering
Li, D., Chen, C., Liu, W., Lu, T., Gu, N., and Chu, S · 2017
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Ermma: Expected risk minimization for matrix approximation-based recommender systems
Li, D., Chen, C., Lv, Q., Shang, L., Chu, S., and Zha, H · 2017
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Convex factorization machine for toxicogenomics prediction
Yamada, M., Lian, W., Goyal, A., Chen, J., Wimalawarne, K., Khan, S. A., Kaski, S., Mamitsuka, H., and Chang, Y · 2017
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Adaerror: An adaptive learning rate method for matrix approximation-based collaborative filtering
Li, D., Chen, C., Lv, Q., Gu, H., Lu, T., Shang, L., Gu, N., and Chu, S. M · 2018
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