An Improvement of Matrix Factorization with Bound Constraints for Recommender Systems

2016 ◽  
Author(s):  
Kazuki Mori ◽  
Tung Nguyen ◽  
Tomohiro Harada ◽  
Ruck Thawonmas
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Bound Constraints

BaPa: A Novel Approach of Improving Load Balance in Parallel Matrix Factorization for Recommender Systems

2020 ◽  
pp. 1-1
Author(s):  
Ruixin Guo ◽  
Feng Zhang ◽  
Lizhe Wang ◽  
Wusheng Zhang ◽  
Xinya Lei ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Load Balance ◽  
Novel Approach

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.

Counteracting the filter bubble in recommender systems: Novelty-aware matrix factorization

2019 ◽  
Vol 13 (1) ◽  
pp. 37-47
Author(s):  
Panagiotis Symeonidis ◽  
Ludovik Coba ◽  
Markus Zanker
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Filter Bubble
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