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Scientific Programming
Volume 2018, Article ID 2181974, 8 pages
Research Article

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Yan Li1 and Yan Guo2

1School of Business and Management, Shanghai International Studies University, Shanghai, China
2State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

Correspondence should be addressed to Yan Guo; nc.ude.tpub@nayoug

Received 13 October 2017; Accepted 26 December 2017; Published 14 February 2018

Academic Editor: Youngjae Kim

Copyright © 2018 Yan Li and Yan Guo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.