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 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.
College of Computer Science, Zhejiang University, Hangzhou 310027, China.
Qu
Q
Li
College of Computer Science, Zhejiang University, Hangzhou 310027, China.
Min
0000-0002-2966-6928
M
Yao
College of Computer Science, Zhejiang University, Hangzhou 310027, China.
Jianhua
J
Yang
College of Computer Science, Zhejiang University, Hangzhou 310027, China.
Ning
N
Xu
D000465:Algorithms; D033062:Friends; D006801:Humans; D008957:Models, Genetic; D008962:Models, Theoretical; D011897:Random Allocation; D061108:Social Media / Q000706:statistics & numerical data*