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Personalized Service Recommendation for Mobile Edge Computing Environment

모바일 엣지 컴퓨팅 환경에서의 개인화 서비스 추천

  • Yim, Jong-choul (Electronics and Telecommunications Research Institute) ;
  • Kim, Sang-ha (Chungnam National Univ. Department of Computer Science & Engineering) ;
  • Keum, Chang-sup (Electronics and Telecommunications Research Institute)
  • Received : 2016.12.08
  • Accepted : 2017.05.17
  • Published : 2017.05.31

Abstract

Mobile Edge Computing(MEC) is a emerging technology to cope with mobile traffic explosion and to provide a variety of services having specific requirements by means of running some functions at mobile edge nodes directly. For instance, caching function can be executed in order to offload mobile traffics, and safety services using real time video analytics can be delivered to users. So far, a myriad of methods and architectures for personalized service recommendation have been proposed, but there is no study on the subject which takes unique characteristics of mobile edge computing into account. To provide personalized services, acquiring users' context is of great significance. If the conventional personalized service model, which is server-side oriented, is applied to the mobile edge computing scheme, it may cause context isolation and privacy issues more severely. There are some advantages at mobile edge node with respect to context acquisition. Another notable characteristic at MEC scheme is that interaction between users and applications is very dynamic due to temporal relation. This paper proposes the local service recommendation platform architecture which encompasses these characteristics, and also discusses the personalized service recommendation mechanism to be able to mitigate context isolation problem and privacy issues.

Acknowledgement

Grant : 단말 근접 실시간 스마트 서비스추천플랫폼 기술 개발

Supported by : 한국전자통신연구원

References

  1. S.-Q. Lee and J. Kim, "Local breakout of mobile access network traffic by mobile edge computing," ICTC 2016, pp. 741-743, Jeju, Oct. 2016.
  2. ETSI, Mobile Edge Computing (MEC); Technical Requirements, ETSI GS MEC 002 V1.1.1, Mar. 2016.
  3. D. Sabella, et al., "Mobile-edge computing architecture: The role of MEC in the internet of things," IEEE Consumer Electron. Mag., vol. 5, no. 4, pp. 84-91, Oct. 2016. https://doi.org/10.1109/MCE.2016.2590118
  4. W. Shi, et al., "Edge computing: Vision and challenges," IEEE Internet of Things J., vol. 3, no. 4, pp. 637-646, Oct. 2016. https://doi.org/10.1109/JIOT.2016.2579198
  5. M. H. ur Rehman, et al., "Opportunistic computation offloading in mobile edge cloud computing environments," 2016 17th IEEE Int. Conf. Mob. Data Management, pp. 208-213, Jun. 2016.
  6. M. Sapienza, et al., "Solving critical events through mobile edge computing: An approach for smart cities," 2016 17th IEEE Int. Conf. Smart Computing, pp. 637-646, Oct. 2016.
  7. S. K. Lee, Y. H. Cho, and S. H. Kim, "Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations," Information Sci., vol. 180, no. 11, pp. 2142-2155, 2010. https://doi.org/10.1016/j.ins.2010.02.004
  8. C. W. Leung, et. al., "TV program recommendation for multiple viewers based on user profile merging," User Modeling and User-Adapted Interaction, vol. 16, no. 1, pp. 63-82, 2006. https://doi.org/10.1007/s11257-006-9005-6
  9. K. Li and T. C. Du, "Building a targeted mobile advertising system for location based services," Decision Support Systems, vol. 54, no. 1, pp. 1-8, 2012. https://doi.org/10.1016/j.dss.2012.02.002
  10. Y. H. Cho and J. K. Kim, "Application of web usage mining and product taxonomy to collaborative filtering in e-commerce," Expert Syst. Appl., vol. 26, pp. 233-246, 2004. https://doi.org/10.1016/S0957-4174(03)00138-6
  11. B. Lika, et al., "Facing the cold start problem in recommender systems," Expert Syst. Appl., vol. 41, pp. 2065-2073, 2014. https://doi.org/10.1016/j.eswa.2013.09.005
  12. E. Toch, et al., "Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems," User Modeling and User-Adapted Interaction, vol. 22, no. 1, pp. 203-220, Apr. 2012. https://doi.org/10.1007/s11257-011-9110-z
  13. IETF, Service Location Protocol, Version 2, IETF RFC 2165, Jun. 1999.
  14. IETF, Multicast DNS, IETF RFC 6762, Feb. 2013.
  15. J. C. Yim and C. H. Keum, "Technology trends on proximity services," Electron. Telecommun. Trends, vol. 30, no. 1, Jan. 2015.
  16. M. del Carmen Rodriguez-Hernandez and S. Ilarri, "Toward a context-aware mobile recommendation architecture," MobiWiS 2014, pp. 56-70, 2014.