Personalized Location-Aware QoS Prediction for Web Services Using Probabilistic Matrix Factorization

Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.

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Location-Aware Service Recommendation With Enhanced Probabilistic Matrix Factorization

IEEE Access ◽

10.1109/access.2018.2877137 ◽

2018 ◽

Vol 6 ◽

pp. 62815-62825 ◽

Cited By ~ 74

Author(s):

Yuyu Yin ◽

Lu Chen ◽

yueshen xu ◽

Jian Wan

Keyword(s):

Matrix Factorization ◽

Service Recommendation ◽

Location Aware ◽

Probabilistic Matrix Factorization

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Similarity-maintaining Privacy Preservation and Location-aware Low-rank Matrix Factorization for QoS Prediction based Web Service Recommendation

IEEE Transactions on Services Computing ◽

10.1109/tsc.2018.2839741 ◽

2019 ◽

pp. 1-1 ◽

Cited By ~ 2

Author(s):

Xiaoke Zhu ◽

Xiao-Yuan Jing ◽

Di Wu ◽

Zhenyu He ◽

Jicheng Cao ◽

...

Keyword(s):

Web Service ◽

Matrix Factorization ◽

Privacy Preservation ◽

Low Rank ◽

Service Recommendation ◽

Qos Prediction ◽

Location Aware ◽

Rank Matrix ◽

Low Rank Matrix

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From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

Mobile Information Systems ◽

10.1155/2019/8950508 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Shun Li ◽

Junhao Wen ◽

Xibin Wang

Keyword(s):

Matrix Factorization ◽

Recommendation System ◽

Mobile Services ◽

End Users ◽

Mobile Service ◽

Service Recommendation ◽

Qos Prediction ◽

Hybrid Matrix ◽

Location Aware ◽

The Impact

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

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