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...
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Format: | Article |
Language: | English |
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Wiley
2014-01-01
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Series: | The Scientific World Journal |
Online Access: | http://dx.doi.org/10.1155/2014/162148 |
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author | Qu Li Min Yao Jianhua Yang Ning Xu |
author_facet | Qu Li Min Yao Jianhua Yang Ning Xu |
author_sort | Qu Li |
collection | DOAJ |
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. |
format | Article |
id | doaj-art-145d705501bb42fe8715b5bf66670ed8 |
institution | Kabale University |
issn | 2356-6140 1537-744X |
language | English |
publishDate | 2014-01-01 |
publisher | Wiley |
record_format | Article |
series | The Scientific World Journal |
spelling | doaj-art-145d705501bb42fe8715b5bf66670ed82025-02-03T07:24:31ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/162148162148Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend RecommendationQu Li0Min Yao1Jianhua Yang2Ning Xu3College of Computer Science, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science, Zhejiang University, Hangzhou 310027, ChinaCollege of Computer Science, Zhejiang University, Hangzhou 310027, ChinaOnline 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.http://dx.doi.org/10.1155/2014/162148 |
spellingShingle | Qu Li Min Yao Jianhua Yang Ning Xu Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation The Scientific World Journal |
title | 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_short | 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 |
url | http://dx.doi.org/10.1155/2014/162148 |
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