Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations

By Dennis DeCoste. 2006

104 citations recorded by the source corpus.

Subfields: Cancer Research, Computer Vision and Pattern Recognition, Molecular Biology

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Citations in the corpus

Recurrent Latent Variable Networks for Session-Based Recommendation (2017, methodology): “MF-based methods are perhaps the most popular class of model-based CF approaches [9, 21, 26, 27].”
Spectral Regularization Algorithms for Learning Large Incomplete Matrices (2010, methodology): “We will like to note that DeCoste (2006) proposed an efficient implementation of MMMF via an ensemble based approach, which is quite different in spirit from the batch optimization algorithms we are studying in this paper.”
The non-convex geometry of low-rank matrix optimization (2016, background): “These expensive operations form the major computational bottleneck and prevent them from scaling to scenarios with millions of variables, a typical situation in a diverse range of applications, including quantum state tomography [27], user preferences prediction [20], and pairwise distances estimati”
Scalable Collaborative Filtering Approaches for Large Recommender Systems (2009, methodology): “The best known results are given by Delannay and Verleysen (2007): RMSE 0.”
A Survey of Collaborative Filtering Techniques (2009, methodology): “Some new modelbased CF techniques that tackle the sparsity problem include the association retrieval technique, which applies an associative retrieval framework and related spreading activation algorithms to explore transitive associations among users through their rating and purchase history [32]; ”
Recommender Systems (2016, unknown): “The works in [180, 500, 569, 624] explore maximum margin factorization, which is very closely related to unconstrained matrix factorization.”
Non-linear matrix factorization with Gaussian processes (2009, methodology): “Our approach outperforms the URP and Attitude algorithms (Marlin, 2004a), the MMMF (Rennie & Srebro, 2005), E-MMMF of (DeCoste, 2006), and the Item-based approach of (Park & Pennock, 2007) 2004a) users with fewer than 20 ratings were discarded; This left us with 36 , 656 users.”
Collaborative prediction and ranking with non-random missing data (2009, methodology): “However, the statistical theory of missing data developed by Little and Rubin [6] shows that incorrect assumptions about missing data can lead to biased parameter estimation and prediction in a wide range of models and methods including clustering [1], matrix factorization [2] and other probabilisti”
Local Low-Rank Matrix Approximation (2013, methodology): “In addition to single matrix factorization, several ensemble models have been proposed for CF. DeCoste (2006) suggested ensembles of MMMF.”
Nonparametric bayesian multitask collaborative filtering (2013, methodology): “Among all model-based CF methods, matrix factorization (MF)-based methods are perhaps the most popular ones in recent years [5, 29, 25, 22, 26, 27, 15].”
Multi-Domain Collaborative Filtering (2010, methodology): “Among all model-based CF methods, matrix factorization methods are perhaps the most popular one in recent years [2, 25, 20, 5, 21, 22, 14, 32, 29].”