Trust-Based Personalized Service Recommendation: A Network Perspective

2014 ◽  
Vol 29 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Shui-Guang Deng ◽  
Long-Tao Huang ◽  
Jian Wu ◽  
Zhao-Hui Wu
2019 ◽  
Vol 100 ◽  
pp. 600-617 ◽  
Author(s):  
Haifang Wang ◽  
Zhongjie Wang ◽  
Sihang Hu ◽  
Xiaofei Xu ◽  
Shiping Chen ◽  
...  

Author(s):  
Hongxia Zhang ◽  
Yanhui Dong ◽  
Yongjin Yang

AbstractWith the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation (MPSR) approach based on user trajectory and quality of service (QoS) predictions. In the proposed method, users trajectory is firstly discovered by a hybrid long-short memory network. Then, given users trajectories, service QoS is predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy through MPSR. Experimental results on a real dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of accuracy in mobile edge computing.


2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Hao Tian ◽  
Peifeng Liang

With the rapid development and extensive application of Web services, various approaches for Web service recommendation have been proposed in the past. However, the traditional methods only utilize the information of the user-service rating matrix but ignore the trust relations between users, so their recommendation precision is often unsatisfactory, and, furthermore, most of these methods lack the ability to distinguish the credibility of recommendation. To address the problems, we proposed a personalized service recommendation based on trust relationship. In particular, our approach takes into account user experience, interest background, recommendation effect, and evaluation tendency in the formalization of trust relationship, and moreover it can filter out useless or suspected services by exploiting trust relationships between users. To verify the proposed approach, we conducted experiments by using a real-world Web services set. The experimental results show that our proposed approach leads to a substantial increase in the precision and the credibility of service recommendations.


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