scholarly journals An Extension for Bounded-SVD — A Matrix Factorization Method with Bound Constraints for Recommender Systems

2016 ◽  
Vol 24 (2) ◽  
pp. 314-319 ◽  
Author(s):  
Bang Hai Le ◽  
Kazuki Mori ◽  
Ruck Thawonmas
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Bound Constraints

scholarly journals A Hybrid Recommender System for Gaussian Mixture Model and Enhanced Social Matrix Factorization Technology Based on Multiple Interests

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Rui Chen ◽  
Qingyi Hua ◽  
Quanli Gao ◽  
Ying Xing
Keyword(s):  
Recommender Systems ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Matrix Factorization ◽  
Rapid Development ◽  
Gaussian Mixture ◽  
Factorization Method ◽  
Data Sparsity ◽  
Real World Datasets ◽  
Hybrid Recommender

Recommender systems are recently becoming more significant in the age of rapid development of the information technology and pervasive computing to provide e-commerce users’ appropriate items. In recent years, various model-based and neighbor-based approaches have been proposed, which improve the accuracy of recommendation to some extent. However, these approaches are less accurate than expected when users’ ratings on items are very sparse in comparison with the huge number of users and items in the user-item rating matrix. Data sparsity and high dimensionality in recommender systems have negatively affected the performance of recommendation. To solve these problems, we propose a hybrid recommendation approach and framework using Gaussian mixture model and matrix factorization technology. Specifically, the improved cosine similarity formula is first used to get users’ neighbors, and initial ratings on unrated items are predicted. Second, users’ ratings on items are converted into users’ preferences on items’ attributes to reduce the problem of data sparsity. Again, the obtained user-item-attribute preference data is trained through the Gaussian mixture model to classify users with the same interests into the same group. Finally, an enhanced social matrix factorization method fusing user’s and item’s social relationships is proposed to predict the other unseen ratings. Extensive experiments on two real-world datasets are conducted and the results are compared with the existing major recommendation models. Experimental results demonstrate that the proposed method achieves the better performance compared to other techniques in 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

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.

scholarly journals Lossless Linear Integer signal Resampling

2012 ◽  
pp. 225-233
Author(s):  
S.Raghavendra Prasad ◽  
Dr.P.Ramana Reddy
Keyword(s):  
Matrix Factorization ◽  
High Frequency ◽  
Irreversible Process ◽  
Polynomial Interpolation ◽  
Factorization Method ◽  
Special Cases ◽  
The Matrix ◽  
Frequency Components ◽  
Linear Transform ◽  
Signal Resampling

This paper describes about signal resampling based on polynomial interpolation is reversible for all types of signals, i.e., the original signal can be reconstructed losslessly from the resampled data. This paper also discusses Matrix factorization method for reversible uniform shifted resampling and uniform scaled and shifted resampling. Generally, signal resampling is considered to be irreversible process except in some special cases because of strong attenuation of high frequency components. The matrix factorization method is actually a new way to compute linear transform. The factorization yields three elementary integer-reversible matrices. This method is actually a lossless integer-reversible implementation of linear transform. Some examples of lower order resampling solutions are also presented in this paper.

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