scholarly journals Extended Content-boosted Matrix Factorization Algorithm for Recommender Systems

2014 ◽  
Vol 35 ◽  
pp. 417-426 ◽  
Author(s):  
Oleksandr Krasnoshchok ◽  
Yngve Lamo
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Factorization Algorithm

scholarly journals A Smart Privacy-Preserving Learning Method by Fake Gradients to Protect Users Items in Recommender Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Guixun Luo ◽  
Zhiyuan Zhang ◽  
Zhenjiang Zhang ◽  
Yun Liu ◽  
Lifu Wang
Keyword(s):  
Time Delay ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Privacy Preserving ◽  
Learning Method ◽  
The Real ◽  
Good Convergence ◽  
Factorization Algorithm

In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories and experiments, we show that our algorithm can be reduced to a time-delay SGD, which can be proved to have a good convergence so that the accuracy will not decline. Our algorithm achieves a good tradeoff between the privacy and accuracy.

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

Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study

2020 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Yan Yan ◽  
Mingkui Tan ◽  
Ivor W. Tsang ◽  
Yi Yang ◽  
Qinfeng Shi ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Algorithm Analysis ◽  
Cost Matrix ◽  
Factorization Algorithm ◽  
Memory Cost

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.

Sign in / Sign up

Export Citation Format

Share Document