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The determinantal point process (DPP) is an elegant probabilistic model of repulsion with applications in various machine learning tasks including summarization and search.
On the density of eigenvalues of a random matrix
M. L. Mehta and M. Gaudin · 1960
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The coincidence approach to stochastic point processes
O. Macchi · 1975
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Accelerated greedy algorithms for maximizing submodular set functions
M. Minoux · 1978
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An analysis of approximations for maximizing submodular set functions–I
G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher · 1978
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An exact algorithm for maximum entropy sampling
C. W. Ko, J. Lee, and M. Queyranne · 1995
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The use of MMR, diversity-based reranking for reordering documents and producing summaries
J. Carbonell and J. Goldstein · 1998
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Matrix algorithms, volume 1: Basic decompositions
J. R. Schott · 1999
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The TREC-8 question answering track report
E. M. Voorhees · 1999
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IR evaluation methods for retrieving highly relevant documents
K. Järvelin and J. Kekäläinen · 2000
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Improving recommendation diversity
K. Bradley and B. Smyth · 2001
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Evaluation of item-based top-N recommendation algorithms
G. Karypis · 2001
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Improving recommendation lists through topic diversification
C. N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen · 2005
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Avoiding monotony: Improving the diversity of recommendation lists
M. Zhang and N. Hurley · 2008
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Diversifying search results
R. Agrawal, S. Gollapudi, A. Halverson, and S. Ieong · 2009
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On selecting a maximum volume sub-matrix of a matrix and related problems
A. Çivril and M. Magdon-Ismail · 2009
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Matrix factorization techniques for recommender systems
Y. Koren, R. Bell, and C. Volinsky · 2009
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It takes variety to make a world: Diversification in recommender systems
C. Yu, L. Lakshmanan, and S. Amer-Yahia · 2009
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Structured determinantal point processes
A. Kulesza and B. Taskar · 2010
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Maximizing aggregate recommendation diversity: A graph-theoretic approach
G. Adomavicius and Y. Kwon · 2011
Cited alongside, same era.
The million song dataset
T. Bertin-Mahieux, D. P.W. Ellis, B. Whitman, and P. Lamere · 2011
Cited alongside, same era.
Diversification and refinement in collaborative filtering recommender
R. Boim, T. Milo, and S. Novgorodov · 2011
Cited alongside, same era.
Novelty and diversity in top-N recommendation–analysis and evaluation
N. Hurley and M. Zhang · 2011
Cited alongside, same era.
k-DPPs: Fixed-size determinantal point processes
A. Kulesza and B. Taskar · 2011
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Learning determinantal point processes
A. Kulesza and B. Taskar · 2011
Cited alongside, same era.
Approximate inference for determinantal point processes
J. Gillenwater · 2014
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Expectation-maximization for learning determinantal point processes
J. A. Gillenwater, A. Kulesza, E. Fox, and B. Taskar · 2014
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Diverse sequential subset selection for supervised video summarization
B. Gong, W. L. Chao, K. Grauman, and F. Sha · 2014
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Coverage, redundancy and size-awareness in genre diversity for recommender systems
S. Vargas, L. Baltrunas, A. Karatzoglou, and P. Castells · 2014
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Optimal greedy diversity for recommendation
A. Ashkan, B. Kveton, S. Berkovsky, and Z. Wen · 2015
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A tight linear time (1/2)-approximation for unconstrained submodular maximization
N. Buchbinder, M. Feldman, J. Seffi, and R. Schwartz · 2015
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Item popularity and recommendation accuracy
H. Steck · 2011
Cited alongside, same era.
Fair and balanced: Learning to present news stories
A. Ahmed, C. H. Teo, S. V. N. Vishwanathan, and A. Smola · 2012
Cited alongside, same era.
Max-sum diversification, monotone submodular functions and dynamic updates
A. Borodin, H. C. Lee, and Y. Ye · 2012
Cited alongside, same era.
Near-optimal MAP inference for determinantal point processes
J. Gillenwater, A. Kulesza, and B. Taskar · 2012
Cited alongside, same era.
GenDeR: A generic diversified ranking algorithm
J. He, H. Tong, Q. Mei, and B. Szymanski · 2012
Cited alongside, same era.
Determinantal point processes for machine learning
A. Kulesza and B. Taskar · 2012
Cited alongside, same era.
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Fixed-point algorithms for learning determinantal point processes
Z. Mariet and S. Sra · 2015
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Bayesian low-rank determinantal point processes
M. Gartrell, U. Paquet, and N. Koenigstein · 2016
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Gaussian quadrature for matrix inverse forms with applications
C. Li, S. Sra, and S. Jegelka · 2016
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A coverage-based approach to recommendation diversity on similarity graph
S. A. Puthiya Parambath, N. Usunier, and Y. Grandvalet · 2016
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Adaptive, personalized diversity for visual discovery
C. H. Teo, H. Nassif, D. Hill, S. Srinivasan, M. Goodman, V. Mohan, and S. V. N. Vishwanathan · 2016
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Relevance meets coverage: A unified framework to generate diversified recommendations
L. Wu, Q. Liu, E. Chen, N. J. Yuan, G. Guo, and X. Xie · 2016
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Tweet timeline generation with determinantal point processes
J. G. Yao, F. Fan, W. X. Zhao, X. Wan, E. Y. Chang, and J. Xiao · 2016
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Low-rank factorization of determinantal point processes
M. Gartrell, U. Paquet, and N. Koenigstein · 2017
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Faster greedy MAP inference for determinantal point processes
I. Han, P. Kambadur, K. Park, and J. Shin · 2017
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A single-step approach to recommendation diversification
S. C. Lee, S. W. Kim, S. Park, and D. K. Chae · 2017
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Adapting Markov decision process for search result diversification
L. Xia, J. Xu, Y. Lan, J. Guo, W. Zeng, and X. Cheng · 2017
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