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iALS is a popular algorithm for learning matrix factorization models from implicit feedback with alternating least squares.
Convergence of a block coordinate descent method for nondifferentiable minimization
Tseng, P · 2001
Earlier work this paper cites.
Learning to rank with nonsmooth cost functions
Burges, C., Ragno, R., and Le, Q · 2007
Earlier work this paper cites.
Collaborative filtering for implicit feedback datasets
Hu, Y., Koren, Y., and Volinsky, C · 2008
Earlier work this paper cites.
Fast als-based matrix factorization for explicit and implicit feedback datasets
Pilászy, I., Zibriczky, D., and Tikk, D · 2010
Earlier work this paper cites.
The million song dataset
Bertin-mahieux, T., Ellis, D. P. W., Whitman, B., and Lamere, P · 2011
Earlier work this paper cites.
Slim: Sparse linear methods for top-n recommender systems
Ning, X., and Karypis, G · 2011
Earlier work this paper cites.
Fast context-aware recommendations with factorization machines
Rendle, S., Gantner, Z., Freudenthaler, C., and Schmidt-Thieme, L · 2011
Cited alongside, same era.
Wsabie: Scaling up to large vocabulary image annotation
Weston, J., Bengio, S., and Usunier, N · 2011
Cited alongside, same era.
Scalable coordinate descent approaches to parallel matrix factorization for recommender systems
Yu, H.-F., Hsieh, C.-J., Si, S., and Dhillon, I · 2012
Cited alongside, same era.
Algorithms for nonnegative matrix and tensor factorizations: A unified view based on block coordinate descent framework
Kim, J., He, Y., and Park, H · 2014
Cited alongside, same era.
The movielens datasets: History and context
Harper, F. M., and Konstan, J. A · 2015
Cited alongside, same era.
Fast matrix factorization for online recommendation with implicit feedback
A generic coordinate descent framework for learning from implicit feedback
Bayer, I., He, X., Kanagal, B., and Rendle, S · 2017
Later among the works it cites.
Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S · 2017
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Variational autoencoders for collaborative filtering
Liang, D., Krishnan, R. G., Hoffman, M. D., and Jebara, T · 2018
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Embarrassingly shallow autoencoders for sparse data
Steck, H · 2019
Later among the works it cites.
Item recommendation from implicit feedback
Rendle, S · 2021
Closest in time.
Revisiting the performance of iALS on item recommendation benchmarks, 2021
Rendle, S., Krichene, W., Zhang, L., and Koren, Y · 2021
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He, X., Zhang, H., Kan, M.-Y., and Chua, T.-S · 2016
Cited alongside, same era.
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