A Context-Aware Recommender Engine for Smart Kitchen

  • PratibhaEmail author
  • Pankaj Deep Kaur
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)


Internet of Things (IoT) is the next wave of technological innovation that binds significant number of things (objects) together and produces considerable amount of services which people may use and subscribe for their convenience. As large numbers of objects are interconnected in IoT, collecting information related to the users and their preferences is challenging task. Thus, recommending services to the users based on the objects which are available with them is indispensable for the success of IoT. This paper proposes a context-aware recommender engine for suggesting possible recipes with available food items and time required to prepare the meal in the smart kitchen. It not only considers user’s context but also other environmental context like weather, time, and energy consumption by appliances. The paper concludes with possible future research directions in the area under discussion.


Internet of Things (IoT) Recommender systems (RSs) Context-aware Context-aware recommender system (CARS) 


  1. 1.
    Asabere, N. Y.: Towards a Viewpoint of Context-Aware Recommender Systems (CARS) and Services. Int’l J. of Comp. Sci. and Telecom. vol 4, pp. 19–29 (2013)Google Scholar
  2. 2.
    Ashton, K.: That ’internet of things’ thing in the real world, things matter more than ideas. J. RFID (2009)Google Scholar
  3. 3.
    International Telecommunication Union: The internet of things. Workshop Report, International Telecommunication Union (2005)Google Scholar
  4. 4.
    Lu, T., Neng, W.: Future internet: The internet of things. In: 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vol. 5, pp. 376–380. (2010)Google Scholar
  5. 5.
    Guillemin, P., Friess, P.: Internet of things strategic research roadmap. Technical Report, The Cluster of European Research Projects (2009)Google Scholar
  6. 6.
    Gubbi, J. et al.: Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems. vol. 29, issue 7, pp. 1645–1660 (2013)Google Scholar
  7. 7.
    Bobadilla J., et al.: Recommender Systems Survey. Knowledge based systems. vol. 46, pp. 109–132 (2013)Google Scholar
  8. 8.
    Ansari, A., Essegaier, S. and Kohli, R.: Internet recommendation systems. Journal of Marketing research, 37, pp. 363–375 (2000)Google Scholar
  9. 9.
    Verbert K. et al.: Context-aware recommender system for learning: A survey and future challenges. IEEE transactions on learning technologies. vol. 5, issue 4, pp. 318–335 (2012)Google Scholar
  10. 10.
    Bouneffouf, D. Mobile recommender systems methods: An overview. (2013)Google Scholar
  11. 11.
    Lops P, de Gemmis M, Semeraro G: Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, Berlin, pp 73–105 (2011)Google Scholar
  12. 12.
    Pazzani M and Billsus D: Content-based recommendation systems. In: Brusilovsky P, Kobsa, Nejdl W (eds) The Adaptive Web, vol 4321. Lecture Notes in Computer Science. Springer, Berlin, pp 325–341 (2007)Google Scholar
  13. 13.
    Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T: Recommender systems. Phys Reports 519, pp. 1–49 (2012)Google Scholar
  14. 14.
    Xue GR, Lin C, Yang Q, Xi W, Zeng HJ, Yu Y, Chen Z: Scalable collaborative filtering using cluster-based smoothing. In: 28th annual international ACM SIGIR conference on Research and development in information retrieval, Salvador (2005)Google Scholar
  15. 15.
    Burke R: Hybrid recommender systems: survey and experiments. User Model User Adap Inter 12, pp. 331–370 (2002)Google Scholar
  16. 16.
    Zhang ZK, Zhou T, Zhang YC: Tag-Aware recommender systems: a state-of-the-art survey. J Computer Science Technology, vol. 26, pp. 767–777 (2011)Google Scholar
  17. 17.
    Zhou X, Xu Y, Li Y, Josang A, Cox C. In: The state-of-the-art in personalized recommender systems for social networking. Artificial Intelligence Rev 37, pp. 119–132 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.GNDU Regional CampusJalandharIndia

Personalised recommendations