QoS Value Prediction Using a Combination of Filtering Method and Neural Network Regression

被引:4
作者
Chattopadhyay, Soumi [1 ]
Banerjee, Ansuman [2 ]
机构
[1] Indian Inst Informat Technol, Gauhati, India
[2] Indian Stat Inst, Kolkata, India
来源
SERVICE-ORIENTED COMPUTING (ICSOC 2019) | 2019年 / 11895卷
关键词
D O I
10.1007/978-3-030-33702-5_11
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
With increasing demand and adoption of web services in the world wide web, selecting an appropriate web service for recommendation is becoming a challenging problem to address today. The Quality of Service (QoS) parameters, which essentially represent the performance of a web service, play a crucial role in web service selection. However, obtaining the exact value of a QoS parameter of service before its execution is impossible, due to the variation of the QoS parameter across time and users. Therefore, predicting the value of a QoS parameter has attracted significant research attention. In this paper, we consider the QoS prediction problem and propose a novel solution by leveraging the past information of service invocations. Our proposal, on one hand, is a combination of collaborative filtering and neural network-based regression model. Our filtering approach, on the other hand, is a coalition of the user-intensive and service-intensive models. In the first step of our approach, we generate a set of similar users on a set of similar services. We then employ a neural network-based regression module to predict the QoS value of a target service for a target user. The experiments are conducted on the WS-DREAM public benchmark dataset. Experimental results show the superiority of our method over state-of-the-art approaches.
引用
收藏
页码:135 / 150
页数:16
相关论文
共 25 条
[1]   Accurate prediction of solvent accessibility using neural networks-based regression [J].
Adamczak, R ;
Porollo, A ;
Meller, J .
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, 2004, 56 (04) :753-767
[2]  
Amin A., 2012, Proceedings of the 2012 IEEE 19th International Conference on Web Services (ICWS), P74, DOI 10.1109/ICWS.2012.37
[3]  
[Anonymous], 2013, PRINCIPLES ARTIFICIA
[4]  
Breese J. S., 1998, Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998), P43
[5]   A Framework for Top Service Subscription Recommendations for Service Assemblers [J].
Chattopadhyay, Soumi ;
Banerjee, Ansuman ;
Mukherjee, Tridib .
PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2016), 2016, :332-339
[6]   Personalized QoS-Aware Web Service Recommendation and Visualization [J].
Chen, Xi ;
Zheng, Zibin ;
Liu, Xudong ;
Huang, Zicheng ;
Sun, Hailong .
IEEE TRANSACTIONS ON SERVICES COMPUTING, 2013, 6 (01) :35-47
[7]  
Demuth H., 2004, NEURAL NETWORK TOOLB, V4
[8]  
Ester M., 1996, P 2 INT C KNOWL DISC
[9]  
Leñero-Bardallo JA, 2012, IEEE I C ELECT CIRC, P205, DOI 10.1109/ICECS.2012.6463765
[10]   From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System [J].
Li, Shun ;
Wen, Junhao ;
Wang, Xibin .
MOBILE INFORMATION SYSTEMS, 2019, 2019