Context-Aware Location Recommendations for Smart Cities

  • Akanksha PalEmail author
  • Abhishek Singh Rathore
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Machine learning (ML) helps to optimize the quality of service for citizens with low expenses and quick responses on urgent issues. ML techniques have been known for many years and are now capable of helping with the development of solutions to several common issues found in smart cities, including network infrastructure and Internet of Things (IoT) data (Huang et al. Social friend recommendation based on multiple network correlation, 2015). ML provides intelligent network infrastructures with smarter data management and cognitive applications.

Smart city projects address several issues affecting highly inhabited areas and cities. As such, state institutions and private organizations are exploring their potential benefits. In technical terms, smart city projects have a complex set of requirements, including multiple users with extremely different needs.

Recommendation systems have recently gained popularity with researchers because of their versatility with integrating different research areas. A recommendation system generally interacts with its users in the most user-friendly way possible and recommends something based its user’s preferences. Recommendations for news, videos, retail items, or personalized travel can be dealt with by comparative ML algorithms. Moreover, in the recommendation requests, ML algorithms can be adjusted with the help of special query language.

ML provides an attainable approach to resolve multiple demands and objectives while keeping computations on an inexpensive level of difficulty and providing the necessary process capabilities. This permits, for example, the ability to develop “on-the-fly” solutions for issues with decision variables. An ML recommendation system can be created, which can help in the formation of smart cities.

The adoptions of software-defined networking and ML are innovative approaches for smart city project development and preparation. Big data are also considered to be an inherent component of smart city projects that must be tackled. It has been argued that the complexity of smart city projects requires new and innovative approaches that can result in more efficient and intelligent systems. A broad framework is proposed in this chapter, with a discussion about the impacts of software-defined networking and ML on innovation. In addition, a use case is presented to demonstrate the potential benefits of cognitive learning for smart cities.



Internet of Things


Location-Based Social Networks


Machine Learning


  1. 1.
    Guo, L., Zhang, C., & Fang, Y. (2013). A trust-based privacy-preserving friend recommendation scheme for online social networks. Piscataway, NJ: IEEE.Google Scholar
  2. 2.
    Kacchi, T. R., & Deoranker, A. V. (2016). Friend recommendation system based on lifestyles of users. In International Conference on Advances in Electrical, Electronics, Information, Communication and Bioinformatics (AEEICB16) (pp. 01–04).Google Scholar
  3. 3.
    Wang, P. Z., & Qi, H. (2015). Friendbook: A semantic-based friend recommendation system for social networks. IEEE Transactions on Mobile Computing, 14(3), 01–14.CrossRefGoogle Scholar
  4. 4.
    Bian, L., & Holtzman, H. (2011). Online friend recommendation through personality matching and collaborative filtering. In Proceedings of the 5th International Conference on Mobile Ubiquitous Compute, Systems, Services and Technologies (pp. 230–235).Google Scholar
  5. 5.
    Amazon. (2014). [Online]. Available:
  6. 6.
    Netflix. (2014). [Online]. Available:
  7. 7.
    Kwon, J., & Kim, S. (2010). Friend recommendation method using physical and social context. International Journal of Computer Science and Network Security, 10(11), 116–120.Google Scholar
  8. 8.
    Du, Z., Hu, L., Fu, X., & Liu, Y. (2014). Scalable and explainable friend recommendation in campus social network system. Dordrecht: Springer.CrossRefGoogle Scholar
  9. 9.
    Yang, L., Li, B., Zhou, X., & Kang, Y. (2018). Micro-blog friend recommendation algorithms based on content and social relationship. Singapore: Springer.CrossRefGoogle Scholar
  10. 10.
    Zhao, Y., Zhu, J., Jia, M., Yang, W., & Zheng, K. (2017). A novel hybrid friend recommendation framework for Twitter. Cham: Springer.CrossRefGoogle Scholar
  11. 11.
    Raghuwanshi, R., & Prajapati, G. L. (2017). An approach for friend recommendation based on selected attributes. In Proceedings of the World Congress on Engineering 2017, Vol. II WCE 2017, July 5–7, 2017, London, UK.Google Scholar
  12. 12.
    Deng, Z., He, B., Yu, C., & Chen, Y. (2012). Personalized friend recommendation in social network based on clustering method. Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  13. 13.
    Farikha, M., Mhiri, M., & Gargouri, F. An user interest ontology based on trusted friend preferences for personalized recommendation. In EMCIS 2017, LNBIP 299 (pp. 54–67). Cham: Springer.Google Scholar
  14. 14.
    Fei, Y., Che, N., Li, Z., Li, K., & Jiang, S. Friend recommendation considering preference coverage in location-based social networks. In PAKDD 2017, Part II, LNAI 10235 (pp. 91–105). Cham: Springer.Google Scholar
  15. 15.
    Zhang, Z., Zhao, X., & Wang, G. (2017). FE-ELM: A new friend recommendation model with extreme learning machine. New York: Springer.Google Scholar
  16. 16.
    Zhang, Z., Liu, Y., Ding, W., & Huang, W. W. (2015). A friend recommendation system using users’ information of total attributes. In ICDS 2015, LNCS 9208 (pp. 34–41). Cham: Springer.Google Scholar
  17. 17.
    AI-Turjman, F., & AITurjman, S. (2018). Confidential smart-sensing framework in the IoT era. The Springer Journal of Supercomputing, 74(10), 5187–5198.CrossRefGoogle Scholar
  18. 18.
    Alabady, S. A., AI-Turjman, F., & Din, S. (2018). A novel security model for cooperative virtual networks in the IoT era. Springer International Journal of Parallel Programming, 1–16.Google Scholar
  19. 19.
    Nguyen, T. L. T., & Cao, T. H. (2014). Multi-group-based user perceptions for friend recommendation in social networks. In PAKDD 2014, LNAI 8643 (pp. 525–534). Cham: Springer.Google Scholar
  20. 20.
    AI-Turjman, F., & AITurjman, S. (2018). Context-sensitive access in industrial internet of things (IIoT) healthcare applications. IEEE Transactions on Industrial Informatics, 14, 2736–2744.CrossRefGoogle Scholar
  21. 21.
    Kumar, P., & Reddy, G. R. M. (2018). Friendship recommendation system using topological structure of social network. Singapore: Springer.CrossRefGoogle Scholar
  22. 22.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Shri Vaishnav Institute of Information Technology SVVVIndoreIndia

Personalised recommendations