Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend RecommendationReportar como inadecuado




Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

The Scientific World Journal - Volume 2014 2014, Article ID 162148, 5 pages -

Research ArticleCollege of Computer Science, Zhejiang University, Hangzhou 310027, China

Received 11 September 2013; Accepted 11 February 2014; Published 16 March 2014

Academic Editors: F. Yu and G. Yue

Copyright © 2014 Qu Li et al. 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.

Abstract

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.





Autor: Qu Li, Min Yao, Jianhua Yang, and Ning Xu

Fuente: https://www.hindawi.com/



DESCARGAR PDF




Documentos relacionados