Analysis of GA Optimized ANN for Proactive Context Aware Recommender System

  • Akshi Kumar
  • Nitin SachdevaEmail author
  • Archit Garg
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)


A Recommender System essentially focusses on recommending the most significant items to the users based on their preferences. The objective of a recommender system is to create relevant suggestions to the users for the items like foods for a restaurant etc. based on their interests. The traditional recommender systems put forward recommendations to the users without taking in account any of the considerations about the contextual information like time and place etc. This paper explicates a way to deal with restaurant based recommender system by utilizing a hybrid approach namely ‘genetic algorithm optimized artificial neural network’ to yield higher precision and accuracy in prescribing relevant items to the users based on their preferences and interests. Here, a system of a ‘context aware recommender system’ is implemented that suggests diverse sorts of things proactively to the users. This system involves implementation of the artificial neural network technique that will do the reasoning of the context to figure out whether to throw a recommendation or not and what kind of items to prescribe to the users proactively depending on their interests. The artificial neural network inputs are virtually taken from the Internet of things and its outputs are the scores based on the type of recommendations. These scores have been utilized to choose whether to throw a recommendation or not. This study represents strong potential for prescribing relevant items to the users utilizing the hybrid methodology of “genetic algorithm optimized artificial neural network” for ‘context aware recommender system’ with higher precision and accuracy.


Artificial neural network Context aware recommender systems Genetic algorithm Internet of things 


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Singh, A., Sharma, A., Dey, N., Ashour, A.S.: Web recommendation techniques-status, issues and challenges. J. Netw. Commun. Emerg. Technol. 5(2), 57–65 (2015)Google Scholar
  3. 3.
    Shendage, P.N.: Review on collaborative filtering and web services recommendation. Int. J. Eng. Res. Gen. Sci. 2(6), 912–916 (2014)Google Scholar
  4. 4.
    Anandakumar, K., Rathipriya, K., Bharathi, A.: A survey on methodologies for personalized e-Learning recommender systems. Int. J. Innov. Res. Comput. Commun. Eng. 2(6), 4738–4743 (2014)Google Scholar
  5. 5.
    Beel, J., Genzmehr, M., Langer, S., Nürnberger, A., Gipp, B.: A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. In: 13th Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation, pp. 7–14. ACM (2013)Google Scholar
  6. 6.
    Salman, Y., Abu-Issa, A., Tumar, I., Hassouneh, Y.: A proactive multi-type context-aware recommender system in the environment of Internet of things. In: Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), pp. 351–355. IEEE (2015)Google Scholar
  7. 7.
    Rathod, P.B., Khodke, P.A.: Genetic algorithm based similarity transitivity in collaborative filtering. Int. J. Eng. Res. Technol. 2(12), 2933–2936 (2013)Google Scholar
  8. 8.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253 (2011)Google Scholar
  9. 9.
    Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5(4), 318–335 (2012)CrossRefGoogle Scholar
  10. 10.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Proceedings of ACM Conference on Recommender Systems, pp. 335–336. ACM (2008)Google Scholar
  11. 11.
    Li, Y., Nie, J., Zhang, Y., Wang, B., Yan, B., Weng, F.: Contextual recommendation based on text mining. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters, pp. 692–700. ACM (2010)Google Scholar
  12. 12.
    Liu, H., Zhang, H., Hui, K., He, H.: Overview of context-aware recommender system research. In: 3rd International Conference on Mechatronics, Robotics and Automation (ICMRA 2015), pp. 1218–1221. Atlantis Press (2015)Google Scholar
  13. 13.
    Roy, A., Tavana, M., Banerjee, S., Caprio, D.D.: A secured context-aware tourism recommender system using artificial bee colony and simulated annealing. Int. J. Appl. Manage. Sci. 8(2), 93–113 (2016)CrossRefGoogle Scholar
  14. 14.
    Liu, Z., Ma, J., Jiang, Z., Miao, Y.: FCT: a fully-distributed context-aware trust model for location based service recommendation. Sci. China Inf. Sci. 60(8), (2017)Google Scholar
  15. 15.
    Hwang, C.S., Su, Y.C., Tseng, K.C.: Using genetic algorithms for personalized recommendation. In: International Conference on Computational Collective Intelligence, pp. 104–112. Springer, Berlin, Heidelberg (2010)Google Scholar
  16. 16.
    Kim, K.J., Ahn, H.: Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. In: International Conference on AI, Simulation, and Planning in High Autonomy Systems, pp. 409–415. Springer, Berlin, Heidelberg (2004)Google Scholar
  17. 17.
    Athani, M., Pathak, N., Khan, A.U.: Dynamic music recommender system using genetic algorithm. Int. J. Eng. Adv. Technol. 3(4), 230–232 (2014)Google Scholar
  18. 18.
    Silva, N.B., Tsang, R., Cavalcanti, G.D., Tsang, J.: A graph-based friend recommendation system using genetic algorithm. In: Evolutionary Computation (CEC), 2010 IEEE Congress, pp. 1–7. IEEE (2010)Google Scholar
  19. 19.
    Zheng, B., Thompson, K., Lam, S.S., Yoon, S.W., Gnanasambandam, N.: Customers’ behavior prediction using artificial neural network. In: IIE Annual Conference. Proceedings, pp. 700–709. Institute of Industrial and Systems Engineers (IISE) (2013)Google Scholar
  20. 20.
    Patil, S., Mane, Y., Dabre, K., Dewan, P., Kalbande, D.: An efficient recommender system using collaborative filtering methods with k-separability approach. Int. J. Eng. Res. Appl. 1, 30–35 (2012)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Delhi Technological UniversityDelhiIndia

Personalised recommendations