![](/images/graphics-bg.png)
Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation
Joint Authors
Li, Qu
Xu, Ning
Yang, Jianhua
Yao, Min
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-16
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Online friend recommendation is a fast developing topic in web mining.
In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy.
To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector.
At the same time, we used graph theory to partition communities with fairly low time and space complexity.
What is more, matrix factorization can combine online and offline recommendation.
Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.
American Psychological Association (APA)
Li, Qu& Yao, Min& Yang, Jianhua& Xu, Ning. 2014. Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-1048533
Modern Language Association (MLA)
Li, Qu…[et al.]. Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation. The Scientific World Journal No. 2014 (2014), pp.1-5.
https://search.emarefa.net/detail/BIM-1048533
American Medical Association (AMA)
Li, Qu& Yao, Min& Yang, Jianhua& Xu, Ning. Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-5.
https://search.emarefa.net/detail/BIM-1048533
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1048533