Accommodation recommendation system from user reviews based on feature-based weighted non-negative matrix factorization method

2015 ◽  
Author(s):  
Thanatcha Lerttripinyo ◽  
Pariyawit Jatukannyaprateep ◽  
Nakornthip Prompoon ◽  
Chate Pattanothai
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Factorization Method ◽  
User Reviews ◽  
Feature Based ◽  
Non Negative Matrix Factorization

An Improved Non-negative Matrix Factorization Method for Dynamic Industrial Fault Diagnosis

2020 ◽  
Vol 53 (7) ◽  
pp. 321-326
Author(s):  
XiaoFeng Gong ◽  
Dongdong Sun ◽  
Zuodong Tang ◽  
Kai Zhou ◽  
RuiSen Luo
Keyword(s):  
Fault Diagnosis ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Non Negative Matrix Factorization

scholarly journals Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianfang Tang ◽  
Lijun Cai ◽  
Yajie Meng ◽  
JunLin Xu ◽  
Changcheng Lu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
De Novo ◽  
Characteristic Curve ◽  
Drug Repurposing ◽  
Factorization Method ◽  
Therapeutic Drugs ◽  
Indicator Matrix ◽  
Novel Coronavirus ◽  
Approved Drugs ◽  
Non Negative Matrix Factorization

A novel coronavirus, named COVID-19, has become one of the most prevalent and severe infectious diseases in human history. Currently, there are only very few vaccines and therapeutic drugs against COVID-19, and their efficacies are yet to be tested. Drug repurposing aims to explore new applications of approved drugs, which can significantly reduce time and cost compared with de novo drug discovery. In this study, we built a virus-drug dataset, which included 34 viruses, 210 drugs, and 437 confirmed related virus-drug pairs from existing literature. Besides, we developed an Indicator Regularized non-negative Matrix Factorization (IRNMF) method, which introduced the indicator matrix and Karush-Kuhn-Tucker condition into the non-negative matrix factorization algorithm. According to the 5-fold cross-validation on the virus-drug dataset, the performance of IRNMF was better than other methods, and its Area Under receiver operating characteristic Curve (AUC) value was 0.8127. Additionally, we analyzed the case on COVID-19 infection, and our results suggested that the IRNMF algorithm could prioritize unknown virus-drug associations.

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.

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