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Embedding based models have been the state of the art in collaborative filtering for over a decade.
Learning polynomials with neural networks
Andoni, A., Panigrahy, R., Valiant, G., and Zhang, L · 1916
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Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
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Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., White, H., et al · 1989
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Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R · 1993
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A neural probabilistic language model
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C · 2003
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An investigation of practical approximate nearest neighbor algorithms
Liu, T., Moore, A. W., Gray, A., and Yang, K · 2004
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Improving regularized singular value decomposition for collaborative filtering
Paterek, A · 2007
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Collaborative filtering for implicit feedback datasets
Hu, Y., Koren, Y., and Volinsky, C · 2008
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The bellkor solution to the netflix grand prize, 2009
Koren, Y · 2009
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Advances in Collaborative Filtering
Koren, Y., and Bell, R · 2011
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Slim: Sparse linear methods for top-n recommender systems
Ning, X., and Karypis, G · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Efficient top-n recommendation by linear regression
Levy, M., and Jack, K · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
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Asymmetric lsh (alsh) for sublinear time maximum inner product search (mips)
Shrivastava, A., and Li, P · 2014
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Neural network matrix factorization, 2015
Dziugaite, G. K., and Roy, D. M · 2015
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Learning image and user features for recommendation in social networks
Geng, X., Zhang, H., Bian, J., and Chua, T · 2015
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The movielens datasets: History and context
Harper, F. M., and Konstan, J. A · 2015
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Deep neural networks for youtube recommendations
Covington, P., Adams, J., and Sargin, E · 2016
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Outer product-based neural collaborative filtering
He, X., Du, X., Wang, X., Tian, F., Tang, J., and Chua, T.-S · 2018
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Leveraging meta-path based context for top- n recommendation with a neural co-attention model
Hu, B., Shi, C., Zhao, W. X., and Yu, P. S · 2018
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Neural personalized ranking for image recommendation
Niu, W., Caverlee, J., and Lu, H · 2018
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A convergence theory for deep learning via over-parameterization
Allen-Zhu, Z., Li, Y., and Song, Z · 2019
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A troubling analysis of reproducibility and progress in recommender systems research, 2019
Dacrema, M. F., Boglio, S., Cremonesi, P., and Jannach, D · 2019
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Gradient descent finds global minima of deep neural networks
Du, S., Lee, J., Li, H., Wang, L., and Zhai, X · 2019
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al · 2016
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.
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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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Latent cross: Making use of context in recurrent recommender systems
Beutel, A., Covington, P., Jain, S., Xu, C., Li, J., Gatto, V., and Chi, E. H · 2018
Cited alongside, same era.
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Mlperf training benchmark, 2019
Mattson, P., Cheng, C., Coleman, C., Diamos, G., Micikevicius, P., Patterson, D., Tang, H., Wei, G.-Y., Bailis, P., Bittorf, V., Brooks, D., Chen, D., Dutta, D., Gupta, U., Hazelwood, K., Hock, A., Huang, X., Ike, A., Jia, B., Kang, D., Kanter, D., Kumar, N., Liao, J., Ma, G., Narayanan, D., Oguntebi, T., Pekhimenko, G., Pentecost, L., Reddi, V. J., Robie, T., John, T. S., Tabaru, T., Wu, C.-J., Xu, L., Yamazaki, M., Young, C., and Zaharia, M · 2019
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On the difficulty of evaluating baselines: A study on recommender systems
Rendle, S., Zhang, L., and Koren, Y · 2019
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A pre-filtering approach for incorporating contextual information into deep learning based recommender systems
Jawarneh, I. M. A., Bellavista, P., Corradi, A., Foschini, L., Montanari, R., Berrocal, J., and Murillo, J. M · 2020
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Sequential recommendation with dual side neighbor-based collaborative relation modeling
Qin, J., Ren, K., Fang, Y., Zhang, W., and Yu, Y · 2020
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Learning a joint search and recommendation model from user-item interactions
Zamani, H., and Croft, W. B · 2020
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Improving the estimation of tail ratings in recommender system with multi-latent representations
Zhao, X., Zhu, Z., Zhang, Y., and Caverlee, J · 2020
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