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Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications.
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.
Factorization meets the neighborhood: A multifaceted collaborative filtering model
Koren, Y · 2008
Earlier work this paper cites.
Large-scale parallel collaborative filtering for the netflix prize
Zhou, Y., Wilkinson, D., Schreiber, R., and Pan, R · 2008
Earlier work this paper cites.
Performance of recommender algorithms on top-n recommendation tasks
Cremonesi, P., Koren, Y., and Turrin, R · 2010
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.
Advances in Collaborative Filtering
Koren, Y., and Bell, R · 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.
Wsabie: Scaling up to large vocabulary image annotation
Weston, J., Bengio, S., and Usunier, N · 2011
Earlier work this paper cites.
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Hidasi, B., and Tikk, D · 2012
Cited alongside, same era.
Learning image and user features for recommendation in social networks
Geng, X., Zhang, H., Bian, J., and Chua, T · 2015
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
He, X., Zhang, H., Kan, M.-Y., and Chua, T.-S · 2016
Cited alongside, same era.
Collaborative denoising auto-encoders for top-n recommender systems
Wu, Y., DuBois, C., Zheng, A. X., and Ester, M · 2016
Cited alongside, same era.
A generic coordinate descent framework for learning from implicit feedback
Bayer, I., He, X., Kanagal, B., and Rendle, S · 2017
Embarrassingly shallow autoencoders for sparse data
Steck, H · 2019
Later among the works it cites.
On sampled metrics for item recommendation
Krichene, W., and Rendle, S · 2020
Later among the works it cites.
Ract: Toward amortized ranking-critical training for collaborative filtering
Lobel, S., Li, C., Gao, J., and Carin, L · 2020
Later among the works it cites.
Neural collaborative filtering vs. matrix factorization revisited
Rendle, S., Krichene, W., Zhang, L., and Anderson, J · 2020
Later among the works it cites.
Recvae: A new variational autoencoder for top-n recommendations with implicit feedback
Shenbin, I., Alekseev, A., Tutubalina, E., Malykh, V., and Nikolenko, S. I · 2020
Later among the works it cites.
Reenvisioning the comparison between neural collaborative filtering and matrix factorization
Anelli, V. W., Bellogín, A., Di Noia, T., and Pomo, C · 2021
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Cited alongside, same era.
Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S · 2017
Cited alongside, same era.
Variational autoencoders for collaborative filtering
Liang, D., Krishnan, R. G., Hoffman, M. D., and Jebara, T · 2018
Cited alongside, same era.
Enhancing vaes for collaborative filtering: Flexible priors & gating mechanisms
Kim, D., and Suh, B · 2019
Cited alongside, same era.
On the difficulty of evaluating baselines: A study on recommender systems
Rendle, S., Zhang, L., and Koren, Y · 2019
Cited alongside, same era.
Closest in time.
A troubling analysis of reproducibility and progress in recommender systems research
Dacrema, M. F., Boglio, S., Cremonesi, P., and Jannach, D · 2021
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Towards a better understanding of linear models for recommendation
Jin, R., Li, D., Gao, J., Liu, Z., Chen, L., and Zhou, Y · 2021
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Item recommendation from implicit feedback
Rendle, S · 2021
Closest in time.
iALS++: Speeding up matrix factorization with subspace optimization, 2021
Rendle, S., Krichene, W., Zhang, L., and Koren, Y · 2021
Closest in time.