Web service QoS prediction by neighbor information combined non-negative matrix factorization

2016 ◽  
Vol 30 (6) ◽  
pp. 3593-3604 ◽  
Author(s):  
Kai Su ◽  
Liangli Ma ◽  
Bin Xiao ◽  
Huaiqiang Zhang
Keyword(s):  
Web Service ◽  
Matrix Factorization ◽  
Qos Prediction ◽  
Non Negative Matrix Factorization

Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization

2013 ◽  
Vol 6 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Zibin Zheng ◽  
Hao Ma ◽  
Michael R. Lyu ◽  
Irwin King
Keyword(s):  
Web Service ◽  
Matrix Factorization ◽  
Qos Prediction

scholarly journals General Collaborative Filtering for Web Service QoS Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Wenming Ma ◽  
Rongjie Shan ◽  
Mingming Qi
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Matrix Factorization ◽  
Experimental Studies ◽  
Low Rank ◽  
Qos Prediction ◽  
Latent Vector ◽  
Wide Range ◽  
Regularization Techniques ◽  
Low Dimensional

To avoid the expensive and time-consuming evaluation, collaborative filtering (CF) methods have been widely studied for web service QoS prediction in recent years. Among the various CF techniques, matrix factorization is the most popular one. Much effort has been devoted to improving matrix factorization collaborative filtering. The key idea of matrix factorization is that it assumes the rating matrix is low rank and projects users and services into a shared low-dimensional latent space, making a prediction by using the dot product of a user latent vector and a service latent vector. Unfortunately, unlike the recommender systems, QoS usually takes continuous values with very wide range, and the low rank assumption might incur high bias. Furthermore, when the QoS matrix is extremely sparse, the low rank assumption also incurs high variance. To reduce the bias, we must use more complex assumptions. To reduce the variance, we can adopt complex regularization techniques. In this paper, we proposed a neural network based framework, named GCF (general collaborative filtering), with the dropout regularization, to model the user-service interactions. We conduct our experiments on a large real-world dataset, the QoS values of which are obtained from 339 users on 5825 web services. The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.

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