Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation

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...

Full description

Bibliographic Details
Main Authors:	Li, Qu, Yao, Min, Yang, Jianhua, Xu, Ning
Format:	Online
Language:	English
Published:	Hindawi Publishing Corporation 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976802/

id	pubmed-3976802
recordtype	oai_dc
spelling	pubmed-39768022014-04-22 Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation Li, Qu Yao, Min Yang, Jianhua Xu, Ning Research Article 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. Hindawi Publishing Corporation 2014-03-16 /pmc/articles/PMC3976802/ /pubmed/24757410 http://dx.doi.org/10.1155/2014/162148 Text en Copyright © 2014 Qu Li et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Li, Qu Yao, Min Yang, Jianhua Xu, Ning
spellingShingle	Li, Qu Yao, Min Yang, Jianhua Xu, Ning Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation
author_facet	Li, Qu Yao, Min Yang, Jianhua Xu, Ning
author_sort	Li, Qu
title	Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation
title_short	Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation
title_full	Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation
title_fullStr	Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation
title_full_unstemmed	Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation
title_sort	genetic algorithm and graph theory based matrix factorization method for online friend recommendation
description	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.
publisher	Hindawi Publishing Corporation
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3976802/
_version_	1612074559905726464

Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation

Similar Items