Location-based and Time-aware Service Recommendation in Mobile Edge Computing

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.

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Mobile Edge Computing-Enhanced Proximity Detection in Time-Aware Road Networks

IEEE Access ◽

10.1109/access.2019.2937337 ◽

2019 ◽

Vol 7 ◽

pp. 167958-167972 ◽

Cited By ~ 3

Author(s):

Yaqiong Liu ◽

Mugen Peng ◽

Guochu Shou

Keyword(s):

Road Networks ◽

Edge Computing ◽

Mobile Edge Computing ◽

Proximity Detection ◽

Time Aware

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Proximity detection based on mobile edge computing in time-aware road networks

2019 IEEE 30th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) ◽

10.1109/pimrc.2019.8904257 ◽

2019 ◽

Author(s):

Yaqiong Liu ◽

Mugen Peng ◽

Guochu Shou

Keyword(s):

Road Networks ◽

Edge Computing ◽

Mobile Edge Computing ◽

Proximity Detection ◽

Time Aware

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QoS Prediction for Service Recommendation With Features Learning in Mobile Edge Computing Environment

IEEE Transactions on Cognitive Communications and Networking ◽

10.1109/tccn.2020.3027681 ◽

2020 ◽

Vol 6 (4) ◽

pp. 1136-1145

Author(s):

Yuyu Yin ◽

Zengxu Cao ◽

Yueshen Xu ◽

Honghao Gao ◽

Rui Li ◽

...

Keyword(s):

Edge Computing ◽

Mobile Edge Computing ◽

Service Recommendation ◽

Computing Environment ◽

Qos Prediction

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Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Scientific Programming ◽

10.1155/2018/2181974 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 3

Author(s):

Yan Li ◽

Yan Guo

Keyword(s):

Big Data ◽

High Performance ◽

Cultural Distance ◽

Edge Computing ◽

User Preference ◽

Mobile Edge Computing ◽

Service Recommendation ◽

Rating Data ◽

Close Proximity ◽

Real World Datasets

In the era of big data, traditional computing systems and paradigms are not efficient and even difficult to use. For high performance big data processing, mobile edge computing is emerging as a complement framework of cloud computing. In this new computing architecture, services are provided within a close proximity of mobile users by servers at the edge of network. Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference. In this paper, we propose a cultural distance-aware service recommendation approach which focuses on not only the similarity but also the local characteristics and preference of users. Our approach employs the cultural distance to express the user preference and combines it with similarity to predict the user ratings and recommend the services with higher rating. In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach. The experimental results based on real-world datasets show that our approach outperforms the traditional recommendation approaches in terms of the reliability of recommendation.

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Mobility-Aware Personalized Service Recommendation in Mobile Edge Computing

10.21203/rs.3.rs-117144/v1 ◽

2020 ◽

Author(s):

Hongxia Zhang ◽

Yanhui Dong ◽

Yongjin Yang

Keyword(s):

Cloud Computing ◽

Recommendation Systems ◽

Base Station ◽

Edge Computing ◽

Mobile Edge Computing ◽

Service Recommendation ◽

Trajectory Prediction ◽

Personalized Service ◽

Significant Challenge ◽

Short Memory

Abstract With 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 approach based on user trajectory and QoS predictions. In the proposed method, users' trajectory is firstly discovered by hybrid long-short memory networks. Then, given users\' trajectories, service QoSs are predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy based on the aforementioned information. Experimental results based on the real base station dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of the accuracy in mobile edge computing.

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