A Gamified Recommendation Framework

  • Authors

    • Mohamed Amine Talhaoui
    • Abderrahmane Daif
    • Mohamed Azzouazi
    • Younes Oubrahim
    2019-03-01
    https://doi.org/10.14419/ijet.v8i1.11.28092
  • big data, gamification, kappa, lambda, real time, recommendation, system.

  • Abstract

    A recommendation system is intended to provide a user with relevant resources based on their preferences. The latter thus reduces his search time but also receives suggestions from the system to which he would not have spontaneously lent attention. The development of the big data technologies and its popularity have included the creation of such systems. Content-based recommendation systems rely on user-based ratings on a set of documents or items. The objective is then to understand the motivations leading him to judge as relevant or not a given item. The gamification aims to use the game to push consumers to do things they would not have done on their own: Fill out a questionnaire, buy a product, watch advertisements or assimilate information. In this paper we join the fields of gamification and recommendation systems to provide a new method and architecture to recommendation.

     

     

  • References

    1. [1] G. Singh and R. Singh Boparai, “A survey on recommendation system,†IOSR J. Comput. Eng. Ver. V, vol. 17, no. 6, pp. 2278–661, 2015.

      [2] J. P. Verma, B. Patel, and A. Patel, “Big data analysis: Recommendation system with hadoop framework,†in Proceedings - 2015 IEEE International Conference on Computational Intelligence and Communication Technology, CICT 2015, 2015, pp. 92–97.

      [3] A. van den Oord, S. Dieleman, and B. Schrauwen, “Deep content-based music recommendation,†Electron. Inf. Syst. Dep., p. 9, 2013.

      [4] J. Lee, M. Sun, and G. Lebanon, “A Comparative Study of Collaborative Filtering Algorithms,†arXiv Prepr. arXiv1205.3193, pp. 1–27, 2012.

      [5] J. J. Sandvig, B. Mobasher, and R. Burke, “Robustness of collaborative recommendation based on association rule mining,†in Proceedings of the 2007 ACM conference on Recommender systems - RecSys ’07, 2007, p. 105.

      [6] M. A. Talhaoui, H. AIT, R. MOULOUKI, S. NKIRI, and M. AZOUAZI, “An Improved Social Media Analysis on 3 Layers: A Real Time Enhanced Recommendation System,†Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 2, pp. 242–247, 2018.

      [7] N. Marz and J. Warren, Big Data, Principles and best practices of scalable real-time data systems, vol. 37. 2015.

      [8] J. Kreps, “Questioning the Lambda Architecture,†O’Reilly, pp. 1–10, 2014.

      [9] F. Groh, “Gamification: State of the Art Definition and Utilization,†Res. Trends Media Informatics, pp. 39–46, 2012.

      [10] J. T. Kim and W. H. Lee, “Dynamical model for gamification of learning (DMGL),†Multimed. Tools Appl., vol. 74, no. 19, pp. 8483–8493, 2015.

      [11] K. Robson, K. Plangger, J. H. Kietzmann, I. McCarthy, and L. Pitt, “Is it all a game? Understanding the principles of gamification,†Bus. Horiz., vol. 58, no. 4, pp. 411–420, 2015.

      [12] D. W. Mcdonald and M. S. Ackerman, “Expertise recommender: a flexible recommendation system and architecture,†in Proceedings of the ACM conference on Computer supported cooperative work, 2000, pp. 231–240.

      [13] L. Chengzhi, S. Caihong, and Y. Jia, “A hybrid recommendation architecture for mobile commerce system,†in Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008, 2008, vol. 2, pp. 520–526.

      [14] M. Del Carmen Rodríguez-Hernández and S. Ilarri, “Towards a context-aware mobile recommendation architecture,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8640 LNCS, pp. 56–70.

      [15] J. K. Kim, H. K. Kim, and Y. H. Cho, “A user-oriented contents recommendation system in peer-to-peer architecture,†Expert Syst. Appl., vol. 34, no. 1, pp. 300–312, 2008.

      [16] T. M. Amine, D. Abderrahmane, R. Zahira, and A. Mohamed, “Smart Cities and New Technology Trends State of the art and perspectives,†no. July, pp. 282–285, 2016.

      [17] S. Ardchir, M. A. Talhaoui, and M. Azzouazi, “Towards an Adaptive Learning Framework for MOOCs,†2017, pp. 236–251.

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  • How to Cite

    Amine Talhaoui, M., Daif, A., Azzouazi, M., & Oubrahim, Y. (2019). A Gamified Recommendation Framework. International Journal of Engineering & Technology, 8(1.11), 73-77. https://doi.org/10.14419/ijet.v8i1.11.28092

    Received date: 2019-03-01

    Accepted date: 2019-03-01

    Published date: 2019-03-01