Estimating Local Information Trustworthiness via Multi-source Joint Matrix Factorization

2012 ◽  
Author(s):  
Liang Ge ◽  
Jing Gao ◽  
Xiao Yu ◽  
Wei Fan ◽  
Aidong Zhang
Keyword(s):  
Matrix Factorization ◽  
Local Information ◽  
Joint Matrix Factorization ◽  
Information Trustworthiness

scholarly journals A Self-Orgnizing Model for Peer-to-Peer Systems using Trust Relations

2014 ◽  
Vol 14 (1) ◽  
pp. 5394-5397
Author(s):  
Sourabh S. Mahajan ◽  
S.K. Pathan
Keyword(s):  
Peer To Peer ◽  
Local Information ◽  
Global Information ◽  
Trust Network ◽  
Trust Relationship ◽  
The Past ◽  
Trust Relationships ◽  
Peer To Peer Systems ◽  
Information Trustworthiness

Peer-to-Peer systems enables the interactions of peers to accomplish tasks. Attacks of peers with malicious can be reduced by establishing trust relationship among peers. In this paper we presents algorithms which helps a peer to reason about trustworthiness of other peers based on interactions in the past and recommendations. Local information is used to create trust network of peers and does not need to deal with global information. Trustworthiness of peers in providing services can be describedby Service metric and recommendation metric. Parameters considered for evaluating interactions and recommendations are Recentness, Importance and Peer Satisfaction. Trust relationships helps a good peer to isolate malicious peers.

scholarly journals Joint Matrix Factorization: A Novel Approach for Recommender System

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 224596-224607
Author(s):  
Shaolun Sun ◽  
Yuetong Xiao ◽  
Yue Huang ◽  
Sen Zhang ◽  
Heng Zheng ◽  
...  
Keyword(s):  
Recommender System ◽  
Matrix Factorization ◽  
Novel Approach ◽  
Joint Matrix Factorization

Multi-label crowd consensus via joint matrix factorization

2019 ◽  
Vol 62 (4) ◽  
pp. 1341-1369
Author(s):  
Jinzheng Tu ◽  
Guoxian Yu ◽  
Carlotta Domeniconi ◽  
Jun Wang ◽  
Guoqiang Xiao ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Joint Matrix Factorization

Community detection enhancement using non-negative matrix factorization with graph regularization

2016 ◽  
Vol 30 (20) ◽  
pp. 1650130 ◽  
Author(s):  
Xiao Liu ◽  
Yi-Ming Wei ◽  
Jian Wang ◽  
Wen-Jun Wang ◽  
Dong-Xiao He ◽  
...  
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Local Information ◽  
Superior Performance ◽  
Great Effort ◽  
Step Method ◽  
Graph Regularization ◽  
Detection Enhancement ◽  
Global And Local ◽  
Non Negative Matrix Factorization

Community detection is a meaningful task in the analysis of complex networks, which has received great concern in various domains. A plethora of exhaustive studies has made great effort and proposed many methods on community detection. Particularly, a kind of attractive one is the two-step method which first makes a preprocessing for the network and then identifies its communities. However, not all types of methods can achieve satisfactory results by using such preprocessing strategy, such as the non-negative matrix factorization (NMF) methods. In this paper, rather than using the above two-step method as most works did, we propose a graph regularized-based model to improve, specialized, the NMF-based methods for the detection of communities, namely NMFGR. In NMFGR, we introduce the similarity metric which contains both the global and local information of networks, to reflect the relationships between two nodes, so as to improve the accuracy of community detection. Experimental results on both artificial and real-world networks demonstrate the superior performance of NMFGR to some competing methods.

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