Add to ORKGMatrix factorization is known to be an effective method for recommender systems that are given only the ratings from users to items. Currently, stochastic gradient (SG) method is one of the most popular algorithms for matrix factorization. However, as a sequential approach, SG is difficult to be parallelized for handling web-scale problems. In this paper, we develop a fast parallel SG method, FPSG, for shared memory systems. By dramatically reducing the cache-miss rate and carefully addressing the load balance of threads, FPSG is more efficient than state-of-the-art parallel algorithms for matrix factorization
Alternating least squares (ALS) has been proved to be an effective solver for matrix factorization i...
The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a nu...
We present ‘Factorbird’, a prototype of a parameter server approach for factor-izing large matrices ...
Matrix factorization is known to be an effective method for recommender systems that are given only ...
Matrix factorization is known to be an effective method for recommender systems that are given only ...
在推薦系統上，矩陣分解是一個非常有效的技術。 對於矩陣分解問題，隨機梯度下降法是一個高效的演算法。 然而，這個演算法並不容易被平行。 這篇論文，在共享記憶體系統中，我們開發一個新的平行演算法叫做FPS...
Abstract. Matrix factorization, when the matrix has missing values, has become one of the leading te...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
Matrix factorization is one of the fundamental techniques for analyzing latent relationship between ...
International audienceWe introduce an asynchronous distributed stochastic gradient algorithm for mat...
Stochastic gradient descent (SGD) and its variants have become more and more popular in machine lear...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
Abstract. Stochastic gradient methods are effective to solve matrix fac-torization problems. However...
Boosting is one of the most popular and powerful learning algorithms. However, due to its sequential...
International audienceWe discuss efficient shared memory parallelization of sparse matrix computatio...
Alternating least squares (ALS) has been proved to be an effective solver for matrix factorization i...
The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a nu...
We present ‘Factorbird’, a prototype of a parameter server approach for factor-izing large matrices ...
Matrix factorization is known to be an effective method for recommender systems that are given only ...
Matrix factorization is known to be an effective method for recommender systems that are given only ...
在推薦系統上，矩陣分解是一個非常有效的技術。 對於矩陣分解問題，隨機梯度下降法是一個高效的演算法。 然而，這個演算法並不容易被平行。 這篇論文，在共享記憶體系統中，我們開發一個新的平行演算法叫做FPS...
Abstract. Matrix factorization, when the matrix has missing values, has become one of the leading te...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
Matrix factorization is one of the fundamental techniques for analyzing latent relationship between ...
International audienceWe introduce an asynchronous distributed stochastic gradient algorithm for mat...
Stochastic gradient descent (SGD) and its variants have become more and more popular in machine lear...
As Web 2.0 and enterprise-cloud applications have proliferated, data mining algorithms increasingly ...
Abstract. Stochastic gradient methods are effective to solve matrix fac-torization problems. However...
Boosting is one of the most popular and powerful learning algorithms. However, due to its sequential...
International audienceWe discuss efficient shared memory parallelization of sparse matrix computatio...
Alternating least squares (ALS) has been proved to be an effective solver for matrix factorization i...
The implementation of a vast majority of machine learning (ML) algorithms boils down to solving a nu...
We present ‘Factorbird’, a prototype of a parameter server approach for factor-izing large matrices ...