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Determinantal point processes (DPPs) have attracted significant attention as an elegant model that is able to capture the balance between quality and diversity within sets.
Dppnet: Approximating determinantal point processes with deep networks
Zelda Mariet, Yaniv Ovadia, and Jasper Snoek · 1901
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Using association rules for product assortment decisions: A case study
Tom Brijs, Gilbert Swinnen, Koen Vanhoof, and Geert Wets · 1999
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Retail market basket data set
Tom Brijs · 2003
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Collaborative filtering for implicit feedback datasets
Yifan Hu, Yehuda Koren, and Chris Volinsky · 2008
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Near-optimal sensor placements in Gaussian processes: theory, efficient algorithms and empirical studies
Andreas Krause, Ajit Singh, and Carlos Guestrin · 2008
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Alexei Borodin · 2009
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Improving one-class collaborative filtering by incorporating rich user information
Yanen Li, Jia Hu, ChengXiang Zhai, and Ye Chen · 2010
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu · 2011
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Data mining for the online retail industry: A case study of rfm model-based customer segmentation using data mining
D Chen · 2012
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Determinantal Point Processes for machine learning , volume 5
A. Kulesza and B. Taskar · 2012
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Learning mixtures of submodular shells with application to document summarization
H. Lin and J. Bilmes · 2012
Earlier work this paper cites.
Learning with Determinantal Point Processes
A. Kulesza · 2013
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Learning the parameters of determinantal point process kernels
R. Affandi, E. Fox, R. Adams, and B. Taskar · 2014
Cited alongside, same era.
Approximate Inference for Determinantal Point Processes
J. Gillenwater · 2014
Cited alongside, same era.
Expectation-maximization for learning Determinantal Point Processes
J. Gillenwater, A. Kulesza, E. Fox, and B. Taskar · 2014
Cited alongside, same era.
Large-margin determinantal point processes
Wei-Lun Chao, Boqing Gong, Kristen Grauman, and Fei Sha · 2015
Cited alongside, same era.
Determinantal Point Processes, 2015
Laurent Decreusefond, Ian Flint, Nicolas Privault, and Giovanni Luca Torrisi · 2015
Cited alongside, same era.
Scalable recommendation with hierarchical Poisson factorization
Prem Gopalan, Jake M Hofman, and David M Blei · 2015
Cited alongside, same era.
Bayesian low-rank determinantal point processes
Mike Gartrell, Ulrich Paquet, and Noam Koenigstein · 2016
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Meta-prod2vec: Product embeddings using side-information for recommendation
Flavian Vasile, Elena Smirnova, and Alexis Conneau · 2016
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
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Low-rank factorization of Determinantal Point Processes
Mike Gartrell, Ulrich Paquet, and Noam Koenigstein · 2017
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Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
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Learning Determinantal Point Processes with moments and cycles
John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, and Philippe Rigollet · 2017
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
Cited alongside, same era.
Metadata embeddings for user and item cold-start recommendations
Maciej Kula · 2015
Cited alongside, same era.
Determinantal Point Process models and statistical inference
Frédéric Lavancier, Jesper Møller, and Ege Rubak · 2015
Cited alongside, same era.
Fixed-point algorithms for learning Determinantal Point Processes
Zelda Mariet and Suvrit Sra · 2015
Cited alongside, same era.
Deep neural networks for youtube recommendations
Paul Covington, Jay Adams, and Emre Sargin · 2016
Cited alongside, same era.
Learning Determinantal Point Processes in sublinear time, 2016
Christophe Dupuy and Francis Bach · 2016
Cited alongside, same era.
Deep determinantal point process for large-scale multi-label classification
Pengtao Xie, Ruslan Salakhutdinov, Luntian Mou, and Eric P Xing · 2017
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Deep matrix factorization models for recommender systems
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R Salakhutdinov, and Alexander J Smola · 2017
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Stochastic learning on imbalanced data: Determinantal Point Processes for mini-batch diversification
Cheng Zhang, Hedvig Kjellström, and Stephan Mandt · 2017
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Dynamic Determinantal Point Processes
Takayuki Osogami, Rudy Raymond, Akshay Goel, Tomoyuki Shirai, and Takanori Maehara · 2018
Closest in time.
Practical diversified recommendations on youtube with determinantal point processes
Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H Chi, and Jennifer Gillenwater · 2018
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