An Extended Matrix Factorization Approach for QoS Prediction in Service Selection

2012 ◽  
Author(s):  
Wei Lo ◽  
Jianwei Yin ◽  
Shuiguang Deng ◽  
Ying Li ◽  
Zhaohui Wu
Keyword(s):  
Matrix Factorization ◽  
Service Selection ◽  
Factorization Approach ◽  
Qos Prediction ◽  
Extended Matrix

Time-aware cloud manufacturing service selection using unknown QoS prediction and uncertain user preferences

2021 ◽  
pp. 1063293X2110195
Author(s):  
Ying Yu ◽  
Shan Li ◽  
Jing Ma
Keyword(s):  
Time Series Prediction ◽  
Service Selection ◽  
Cloud Manufacturing ◽  
Service Evaluation ◽  
User Preferences ◽  
User Preference ◽  
Selection Methods ◽  
Qos Prediction ◽  
Qos Performance ◽  
Time Aware

Selecting the most efficient from several functionally equivalent services remains an ongoing challenge. Most manufacturing service selection methods regard static quality of service (QoS) as a major competitiveness factor. However, adaptations are difficult to achieve when variable network environment has significant impact on QoS performance stabilization in complex task processes. Therefore, dynamic temporal QoS values rather than fixed values are gaining ground for service evaluation. User preferences play an important role when service demanders select personalized services, and this aspect has been poorly investigated for temporal QoS-aware cloud manufacturing (CMfg) service selection methods. Furthermore, it is impractical to acquire all temporal QoS values, which affects evaluation validity. Therefore, this paper proposes a time-aware CMfg service selection approach to address these issues. The proposed approach first develops an unknown-QoS prediction model by utilizing similarity features from temporal QoS values. The model considers QoS attributes and service candidates integrally, helping to predict multidimensional QoS values accurately and easily. Overall QoS is then evaluated using a proposed temporal QoS measuring algorithm which can self-adapt to user preferences. Specifically, we employ the temporal QoS conflict feature to overcome one-sided user preferences, which has been largely overlooked previously. Experimental results confirmed that the proposed approach outperformed classical time series prediction methods, and can also find better service by reducing user preference misjudgments.

Personalized LSTM Based Matrix Factorization for Online QoS Prediction

2018 ◽  
Author(s):  
Ruibin Xiong ◽  
Jian Wang ◽  
Zhongqiao Li ◽  
Bing Li ◽  
Patrick C. K. Hung
Keyword(s):  
Matrix Factorization ◽  
Qos Prediction

Multi-user web service selection based on multi-QoS prediction

2013 ◽  
Vol 16 (1) ◽  
pp. 143-152 ◽  
Author(s):  
Shangguang Wang ◽  
Ching-Hsien Hsu ◽  
Zhongjun Liang ◽  
Qibo Sun ◽  
Fangchun Yang
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Qos Prediction ◽  
Web Service Selection
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