scholarly journals A Factorization Machine-Based QoS Prediction Approach for Mobile Service Selection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32961-32970 ◽  
Author(s):  
Mingdong Tang ◽  
Wei Liang ◽  
Yatao Yang ◽  
Jianguo Xie
Keyword(s):  
Service Selection ◽  
Mobile Service ◽  
Qos Prediction ◽  
Factorization Machine ◽  
Prediction Approach

scholarly journals Predicting Quality of Service via Leveraging Location Information

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Liang Chen ◽  
Fenfang Xie ◽  
Zibin Zheng ◽  
Yaoming Wu
Keyword(s):  
Neural Network ◽  
Quality Of Service ◽  
Inner Product ◽  
Location Information ◽  
Network Distance ◽  
Qos Prediction ◽  
Location Aware ◽  
Factorization Machine ◽  
Prediction Approach

QoS (Quality of Service) (our approach can be applied to a wide variety of services; in this paper, we focus on Web services) performance is intensively relevant to locations due to the network distance and the Internet connection between users and services. Thus, considering the location information of services and users is necessary. However, the location information has been ignored by most previous work. In this paper, we take both services’ and users’ location information into account. Specifically, we propose a location-aware QoS prediction approach, called LANFM, by exploiting neural network techniques and factorization machine to improve user-perceived experience. First of all, the information (e.g., id and location) of services and users is expressed as embedding vectors by leveraging neural network techniques. Then, the inner product of various embedding vectors, along with the weighted sum of feature vectors, is used to predict the QoS values. It should be noted that the inner product operation could capture the interactions between services and users, which is helpful to predict QoS values of services that have not been invoked by users. A collection of extensive experiments have been carried out on a real-world dataset to validate the effectiveness of the LANFM model.

An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service selection

2018 ◽  
Vol 108 ◽  
pp. 339-354 ◽  
Author(s):  
Nivethitha Somu ◽  
Gauthama Raman M.R. ◽  
Kalpana V. ◽  
Kannan Kirthivasan ◽  
Shankar Sriram V.S.
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Service Selection ◽  
Cloud Service ◽  
Trust Prediction ◽  
Prediction Approach

scholarly journals Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

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.

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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