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Hybrid Group Recommendation Using Modified Termite Colony Algorithm: A Context Towards Big Data

Author

Listed:
  • Arup Roy

    (Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand 814142, India)

  • Soumya Banerjee

    (Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Jharkhand 814142, India)

  • Chintan Bhatt

    (Department of Computer Science and Engineering, Charotar University of Science and Technology, Changa, Gujarat 388421, India)

  • Youakim Badr

    (CNRS INSA De Lyon, LIRIS Lab, Lyon, UMR-5205, France)

  • Saurav Mallik

    (Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal 700108, India)

Abstract

Since the introduction of Web 2.0, group recommendation systems become an effective tool for consulting and recommending items according to the choices of group of likeminded users. However, the population of dataset consisting of the large number of choices increases the size of storage. As a result, identification of the combination for specific recommendation becomes complex. Hence, the existing group recommendation system should support methodology for handling large data volume with varsity. In this paper, we propose a content-boosted modified termite colony optimisation-based rating prediction algorithm (CMTRP) for group recommendation system. CMTRP employs a hybrid recommendation framework with respect to the big data paradigm to deal with the trend of large data. The framework utilises the communal ratings that help to overcome the scalability problem. The experimental results reveal that CMTRP provides less error in the rating prediction and higher recommendation precision compared with the existing algorithms.

Suggested Citation

  • Arup Roy & Soumya Banerjee & Chintan Bhatt & Youakim Badr & Saurav Mallik, 2018. "Hybrid Group Recommendation Using Modified Termite Colony Algorithm: A Context Towards Big Data," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-31, June.
  • Handle: RePEc:wsi:jikmxx:v:17:y:2018:i:02:n:s0219649218500193
    DOI: 10.1142/S0219649218500193
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    References listed on IDEAS

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    1. B. P. S. Murthi & Sumit Sarkar, 2003. "The Role of the Management Sciences in Research on Personalization," Management Science, INFORMS, vol. 49(10), pages 1344-1362, October.
    2. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
    3. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
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