Large-Scale Parallel Collaborative Filtering for the Netflix Prize

By Yunhong Zhou, Dennis M. Wilkinson, R. Schreiber, Rong Pan. 2008

846 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

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Citations in the corpus

Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012, methodology): “We implemented the alternating least squares (ALS) algorithm [40], a common algorithm in collaborative filtering.”
Petuum: A New Platform for Distributed Machine Learning on Big Data (2013, methodology): “On the other hand, state-of-the-art image recognition systems have now embraced large-scale deep learning models with billions of parameters [14]; topic models with up to 10(6) topics can cover long-tail semantic word sets for substantially improved online advertising [23, 28]; and veryhigh-rank mat”
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System (2019, background): “Low-rank factorization approaches similar to [7], [8] and [9] have been shown to handle sparsity well and are able to scale the retrieval to large datasets.”
Guaranteed Matrix Completion via Non-Convex Factorization (2014, background): “Index Terms —Matrix completion, matrix factorization, nonconvex optimization, alternating minimization, SGD, perturbation analysis.”
Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019, neutral): “All our results go through if these (14)(Candès and Recht, 2009; Zhou et al., 2008).”
Fast and Robust Parallel SGD Matrix Factorization (2015, methodology): “Efficient algorithms for matrix factorization have been developed including alternating least square (ALS) [9, 24], coordinate descent [8, 22], and stochastic gradient descent (SGD) [26, 5].”
A Fast Parallel Stochastic Gradient Method for Matrix Factorization in Shared Memory Systems (2015, methodology): “Many past studies have proposed optimization methods to solve (1), e.g., [Koren et al. 2009; Zhou et al. 2008; Pil´aszy et al. 2010].”
SystemML: Declarative Machine Learning on Spark (2016, methodology): “As our running example, we introduce a DML script of the popular alternating least squares (ALS) algorithm for matrix completion [43].”
Collaborative Filtering with Stacked Denoising AutoEncoders and Sparse Inputs (2015, methodology): “Tikhonov regularization is often used as it empirically provides good results [20].”
PowerLyra: Differentiated Graph Computation and Partitioning on Skewed Graphs (2019, methodology): “Two different collaborative filtering algorithms, Alternating Least Squares (ALS) [99] and Stochastic Gradient Descent (SGD) [69], are used to predict the movie ratings for each user on the Netflix movie recommendation dataset [99], in which the users and movies are presented as vertices, and the…”
Non-convex Optimization for Machine Learning (2017, methodology): “The AM-MC approach is the choice of practitioners in the context of collaborative filtering [Chen and He, 2012, Koren et al., 2009, Zhou et al., 2008].”
Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems (2012, methodology): “8Our C implementation is 6x faster than the MATLAB version in [1].”
A Fast Distributed Stochastic Gradient Descent Algorithm for Matrix Factorization (2014, methodology): “Scholars have proposed many methods for solving Formula (1), e.g. Zhou et al. (2008); Koren et al. (2009); Pilászy et al. (2010).”
A fast parallel SGD for matrix factorization in shared memory systems (2013, background): “Categories and Subject Descriptors G.4 [Mathematics of Computing]: Mathematical Software—Parallel and vector implementations Keywords Recommender system, Matrix factorization, Stochastic gradient descent, Parallel computing, Shared memory algorithm”