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We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms.
Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
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ANF: A Fast and Scalable Tool for Data Mining in Massive Graphs. In Proceedings of the 8th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’02)
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Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations. In Proceedings of the 11th ACM SIGKDD Conference on Knowledge Discovery in Data Mining (KDD ’05)
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Sampling from Large Graphs. In KDD ’06
Jure Leskovec and Christos Faloutsos. 2006 · 2006
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Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition
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Matrix Factorization Techniques for Recommender Systems
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Learning multiple layers of features from tiny images
Alex Krizhevsky. 2009 · 2009
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BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI ’09)
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Data Mining Methods for Recommender Systems
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Learning Attitudes and Attributes from Multi-Aspect Reviews. In ICDM ’12
Julian McAuley, Jure Leskovec, and Dan Jurafsky. 2012 · 2012
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Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose
Fred Morstatter, Jürgen Pfeffer, Huan Liu, and Kathleen Carley. 2013 · 2013
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
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Diederik P. Kingma and Max Welling. 2014 · 2014
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The movielens datasets: History and context
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Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking and Other Missing Label Applications. In Proceedings of the SIGKDD Conference on Knowledge Discovery and Data Mining
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f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. In NeurIPS
S. Nowozin, B. Cseke, and R. Tomioka. 2016 · 2016
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Recommendations as Treatments: Debiasing Learning and Evaluation. In Proceedings of The 33rd International Conference on Machine Learning
T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, and T. Joachims. 2016 · 2016
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On Sampling Strategies for Neural Network-Based Collaborative Filtering. In Proceedings of the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’17)
Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong. 2017 · 2017
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Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17)
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017 · 2017
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Semi-Supervised Classification with Graph Convolutional Networks. In ICLR
Thomas N. Kipf and Max Welling. 2017 · 2017
Energy and Policy Considerations for Deep Learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy
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An Empirical Study of Example Forgetting during Deep Neural Network Learning. In ICLR
M. Toneva, A. Sordoni, R. Combes, A. Trischler, Y. Bengio, and G. Gordon. 2019 · 2019
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Coresets via Bilevel Optimization for Continual Learning and Streaming. In Advances in Neural Information Processing Systems , Vol. 33. Curran Associates, Inc
Zalán Borsos, Mojmir Mutny, and Andreas Krause. 2020 · 2020
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On Target Item Sampling In Offline Recommender System Evaluation. In 14th ACM Conference on Recommender Systems
R. Cañamares and P. Castells. 2020 · 2020
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Selection via Proxy: Efficient Data Selection for Deep Learning. In ICLR
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia. 2020 · 2020
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Cited alongside, same era.
Attention is All you Need. In NeurIPS
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, Ł. Kaiser, and I. Polosukhin. 2017 · 2017
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Self-Attentive Sequential Recommendation. In 2018 IEEE International Conference on Data Mining
W. Kang and J. McAuley. 2018 · 2018
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Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 World Wide Web Conference (WWW ’18)
Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018 · 2018
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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum. 2018 · 2018
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Active Learning for Convolutional Neural Networks: A Core-Set Approach. In ICLR
Ozan Sener and Silvio Savarese. 2018 · 2018
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Item Recommendation on Monotonic Behavior Chains. In Proceedings of the 12th ACM Conference on Recommender Systems
M. Wan and J. McAuley. 2018 · 2018
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Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In KDD ’18
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec. 2018 · 2018
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On Sampled Metrics for Item Recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’20)
Walid Krichene and Steffen Rendle. 2020 · 2020
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MLPerf Training Benchmark. In Proceedings of Machine Learning and Systems
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Exploring Data Splitting Strategies for the Evaluation of Recommendation Models. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20)
Zaiqiao Meng, Richard McCreadie, Craig Macdonald, and Iadh Ounis. 2020 · 2020
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How Useful Are Reviews for Recommendation? A Critical Review and Potential Improvements. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20)
Noveen Sachdeva and Julian McAuley. 2020 · 2020
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Off-Policy Bandits with Deficient Support. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’20)
Noveen Sachdeva, Yi Su, and Thorsten Joachims. 2020 · 2020
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InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang. 2020 · 2020
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Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20)
Ziwei Zhu, Yun He, Yin Zhang, and James Caverlee. 2020 · 2020
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Chasing Carbon: The Elusive Environmental Footprint of Computing. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)
U. Gupta, Y. Kim, S. Lee, J. Tse, H. S. Lee, G. Wei, D. Brooks, and C. Wu. 2021 · 2021
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Semi-supervised batch active learning via bilevel optimization. In 2021 IEEE International Conference on Acoustics, Speech and Signal Processing
Andreas Krause, Marco Tagliasacchi, and Zalán Borsos. 2021 · 2021
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ECLARE: Extreme classification with label graph correlations. In Proceedings of The ACM International World Wide Web Conference
A. Mittal, N. Sachdeva, S. Agrawal, S. Agarwal, P. Kar, and M. Varma. 2021 · 2021
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Carbon Emissions and Large Neural Network Training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. 2021 · 2021
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Sustainable AI: Environmental Implications, Challenges and Opportunities
C. Wu, R. Raghavendra, U. Gupta, B. Acun, N. Ardalani, K. Maeng, G. Chang, F. A. Behram, J. Huang, C. Bai, M. Gschwind, A. Gupta, M. Ott, A. Melnikov, S. Candido, D. Brooks, G. Chauhan, B. Lee, H. S. Lee, B. Akyildiz, M. Balandat, J. Spisak, R. Jain, M. Rabbat, and K. Hazelwood. 2021 · 2021
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