Parallel Factorization Algorithms with Algorithmic Blocking

1999 ◽  
pp. 19-32
Author(s):  
Jaeyoung Choi
Keyword(s):  
Parallel Factorization ◽  
Factorization Algorithms

Factorization Algorithms for Temporal Psychovisual Modulation Display

2016 ◽  
Vol 18 (4) ◽  
pp. 614-626 ◽  
Author(s):  
Zhongpai Gao ◽  
Guangtao Zhai ◽  
Jiantao Zhou
Keyword(s):  
Factorization Algorithms

Faster Lyndon Factorization Algorithms for SLP and LZ78 Compressed Text

2013 ◽  
pp. 174-185 ◽  
Author(s):  
Tomohiro I ◽  
Yuto Nakashima ◽  
Shunsuke Inenaga ◽  
Hideo Bannai ◽  
Masayuki Takeda
Keyword(s):  
Factorization Algorithms

scholarly journals Introduction to Matrix Factorization for Recommender Systems

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Singular Value Decomposition ◽  
Online Education ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Large Scale ◽  
Singular Value ◽  
Factorization Algorithms ◽  
Value Decomposition ◽  
Interaction History

Recommender systems aim to personalize the experience of user by suggesting items to the user based on the preferences of a user. The preferences are learned from the user’s interaction history or through explicit ratings that the user has given to the items. The system could be part of a retail website, an online bookstore, a movie rental service or an online education portal and so on. In this paper, I will focus on matrix factorization algorithms as applied to recommender systems and discuss the singular value decomposition, gradient descent-based matrix factorization and parallelizing matrix factorization for large scale applications.

DATA REPLICATION IN DENSE MATRIX FACTORIZATION

1993 ◽  
Vol 03 (04) ◽  
pp. 419-430 ◽  
Author(s):  
J. MALARD ◽  
C.C. PAIGE
Keyword(s):  
Matrix Factorization ◽  
Message Passing ◽  
Data Replication ◽  
Lu Factorization ◽  
Dense Matrix ◽  
Software Libraries ◽  
Numerical Software ◽  
Factorization Algorithms ◽  
Performance Gains

Gossiping is proposed as the preferred communication primitive for replicating pivot data in dense matrix factorization on message passing multicomputer. Performance gains are demonstrated on a hypercube for LU factorization algorithms based on gossiping as opposed to broadcasting. This finding has consequences for the design of numerical software libraries.

Sign in / Sign up

Export Citation Format

Share Document