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We study the practical consequences of dataset sampling strategies on the performance of recommendation algorithms.
The pagerank citation ranking: Bringing order to the web
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Sampling from large graphs
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Matrix factorization techniques for recommender systems
Koren, Y., Bell, R., and Volinsky, C · 2009
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Bpr: Bayesian personalized ranking from implicit feedback
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Semi-supervised batch active learning via bilevel optimization
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Empirical evaluation of gated recurrent neural networks on sequence modeling, 2014
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Auto-Encoding Variational Bayes
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The movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2015
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Extreme multi-label loss functions for recommendation, tagging, ranking and other missing label applications
Jain, H., Prabhu, Y., and Varma, M · 2016
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On sampling strategies for neural network-based collaborative filtering
Chen, T., Sun, Y., Shi, Y., and Hong, L · 2017
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Neural collaborative filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-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, L. u., and Polosukhin, I · 2017
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Self-attentive sequential recommendation
Kang, W. and McAuley, J · 2018
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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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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
Mitchell, S., Potash, E., Barocas, S., D’Amour, A., and Lum, K · 2018
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Active learning for convolutional neural networks: A core-set approach
Sener, O. and Savarese, S · 2018
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Item recommendation on monotonic behavior chains
Wan, M. and McAuley, J · 2018
Energy and policy considerations for deep learning in NLP
Strubell, E., Ganesh, A., and McCallum, A · 2019
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An empirical study of example forgetting during deep neural network learning
Toneva, M., Sordoni, A., des Combes, R. T., Trischler, A., Bengio, Y., and Gordon, G. J · 2019
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Coresets via bilevel optimization for continual learning and streaming
Borsos, Z., Mutny, M., and Krause, A · 2020
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Selection via proxy: Efficient data selection for deep learning
Coleman, C., Yeh, C., Mussmann, S., Mirzasoleiman, B., Bailis, P., Liang, P., Leskovec, J., and Zaharia, M · 2020
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On sampled metrics for item recommendation
Krichene, W. and Rendle, S · 2020
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Mlperf training benchmark
Mattson, P., Cheng, C., Diamos, G., Coleman, C., 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., Kang, D., Kanter, D., Kumar, N., Liao, J., Narayanan, D., Oguntebi, T., Pekhimenko, G., Pentecost, L., Janapa Reddi, V., Robie, T., St John, T., Wu, C.-J., Xu, L., Young, C., and Zaharia, M · 2020
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Graph convolutional neural networks for web-scale recommender systems
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Are we really making much progress? a worrying analysis of recent neural recommendation approaches
Dacrema, M. F., Cremonesi, P., and Jannach, D · 2019
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Slice: Scalable linear extreme classifiers trained on 100 million labels for related searches
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Learning from less data: A unified data subset selection and active learning framework for computer vision
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Justifying recommendations using distantly-labeled reviews and fine-grained aspects
Ni, J., Li, J., and McAuley, J · 2019
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Sequential variational autoencoders for collaborative filtering
Sachdeva, N., Manco, G., Ritacco, E., and Pudi, V · 2019
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Exploring data splitting strategies for the evaluation of recommendation models
Meng, Z., McCreadie, R., Macdonald, C., and Ounis, I · 2020
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How Useful Are Reviews for Recommendation? A Critical Review and Potential Improvements , pp. 1845–1848
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Off-policy bandits with deficient support
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Debiasing item-to-item recommendations with small annotated datasets
Schnabel, T. and Bennett, P. N · 2020
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Chasing carbon: The elusive environmental footprint of computing
Gupta, U., Kim, Y., Lee, S., Tse, J., Lee, H. S., Wei, G., Brooks, D., and Wu, C · 2021
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Carbon emissions and large neural network training, 2021
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