scholarly journals Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1930
Author(s):  
Alpamis Kutlimuratov ◽  
Akmalbek Abdusalomov ◽  
Taeg Keun Whangbo
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Hierarchical Structures ◽  
The Matrix ◽  
Item Characteristics ◽  
Deep Relationship ◽  
Hierarchical Features ◽  
Novel Model ◽  
Prediction Efficiency ◽  
Non Negative Matrix Factorization

Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.

scholarly journals AANMF: Attribute-Aware Attentional Neural Matrix Factorization

2019 ◽  
Vol 48 (4) ◽  
pp. 682-693
Author(s):  
Bo Zheng ◽  
Jinsong Hu
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Auxiliary Information ◽  
Inner Product ◽  
Data Sets ◽  
It Projects ◽  
Real World Data ◽  
Latent Space ◽  
Almost All ◽  
Novel Model

Matrix Factorization (MF) is one of the most intuitive and effective methods in the Recommendation System domain. It projects sparse (user, item) interactions into dense feature products which endues strong generality to the MF model. To leverage this interaction, recent works use auxiliary information of users and items. Despite effectiveness, irrationality still exists among these methods, since almost all of them simply add the feature of auxiliary information in dense latent space to the feature of the user or item. In this work, we propose a novel model named AANMF, short for Attribute-aware Attentional Neural Matrix Factorization. AANMF combines two main parts, namely, neural-network-based factorization architecture for modeling inner product and attention-mechanism-based attribute processing cell for attribute handling. Extensive experiments on two real-world data sets demonstrate the robust and stronger performance of our model. Notably, we show that our model can deal with the attributes of user or item more reasonably. Our implementation of AANMF is publicly available at https://github.com/Holy-Shine/AANMF.

scholarly journals A factor graph based genetic algorithm

2014 ◽  
Vol 24 (3) ◽  
pp. 621-633 ◽  
Author(s):  
B. Hoda Helmi ◽  
Adel T. Rahmani ◽  
Martin Pelikan
Keyword(s):  
Genetic Algorithm ◽  
Polynomial Time ◽  
Mathematical Analysis ◽  
Matrix Factorization ◽  
Optimization Problems ◽  
Factor Graph ◽  
The Matrix ◽  
Separable Problems ◽  
Linkage Learning ◽  
Non Negative Matrix Factorization

Abstract We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.

scholarly journals Classification of Motor Imagery EEG Based on Sparsification and Non-negative Matrix Factorization

2018 ◽  
Vol 160 ◽  
pp. 07007 ◽  
Author(s):  
Jing Su ◽  
Zuyuan Yang ◽  
Haiping Wang ◽  
Wei Han
Keyword(s):  
Motor Imagery ◽  
Matrix Factorization ◽  
Biomedical Signal Processing ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Left Hand ◽  
The Matrix ◽  
Band Pass ◽  
The Right ◽  
Non Negative Matrix Factorization

The analysis of EEG is a hot topic in the area of biomedical signal processing. In this paper, the EEG signals with Mu (Μ) rhythm and Beta (Β) rhythm are used to solve the motor imagery problem, i.e., the imagery of the left hand and the right hand. The collected raw data is first filtered by FIR band-pass filter, followed by using the maximization of feature difference to increase the sparsity of the matrix. Then, to reduce the redundant information of these features, a non-negative matrix factorization (NMF) method is employed. Due to the usage of the NMF scheme, the obtained factorizations has been better class property. Simulations show that our method achieves higher classification accuracy (more than 91%) than existing results (about 86%).

Non-Negative Matrix Factorization for Blind Source Separation

2020 ◽  
pp. 259-282
Author(s):  
Nabila Aoulass ◽  
Otman Chakkour
Keyword(s):  
Blind Source Separation ◽  
Matrix Factorization ◽  
Multiple Solutions ◽  
Scientific Community ◽  
Initial Conditions ◽  
Source Separation ◽  
Local Minima ◽  
The Matrix ◽  
Observation Matrix ◽  
Non Negative Matrix Factorization

NMF method aim to factorize a non-negative observation matrix X as the product X =G.F between two non-negative matrices G and F, respectively the matrix of contributions and profiles. Although these approaches are studied with great interest by the scientific community, they often suffer from a lack of robustness with regard to data and initial conditions and can present multiple solutions. The work of this chapter aims to examine the different approaches of NMF, thus introducing the constraint of sparsity in order to avoid local minima. The NMF can be informed by introducing desired constraints on the matrix F (resp G) such as the sum of 1 of each of its lines. Applications on images made it possible to test the interest of many algorithms in terms of precision and speed.

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