A Conceptual Model of Hybrid Recommender using Big Data and Machine Learning Approach

Authors

  • M. Venu Gopalachari  Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
  • A Mohan  Department of CSE, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India
  • B S S P Kumar  Department of MCA, Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

Keywords:

Recommender Systems, User Profiling, Content-Based Filtering, Collaborative Filtering, Hybrid Recommender System; E-Tourism

Abstract

An exponential growth in tourism data had recorded online in the past decade due to the recent developments of web technologies and communication means. At the same time, the information overload incurred on web search engines challenges the quality of recommendations to the users although various recommenders have been developed. The main objective of these recommenders is to attract the tourists in turn promote tourism by means of advanced artificial intelligence and big data technologies. In this paper, a conceptual model is proposed for hybrid recommendation system for tourism data that considers the tourist preferences. Hybrid recommender system is the combination of the content based and collaborative filtering recommenders, which absorbs the benefits of both approaches and leads to the quality recommendations. For this, a deep learning model is developed to study the patterns in the tourism data and recommends the based on the tourist profile.

References

  1. L. Sebastia, I. Garc´ıa, E. Onaindia, and C. Guzm´an Alvarez, e-Tourism: A tourist recommendation and planning application, International Journal on Artificial Intelligence Tools, vol. 18, no. 5, pp. 717–738, 2009.
  2. F. Ricci, L. Rokach, and B. Shapira, Introduction to recommender systems handbook, in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. Kantor, eds. Boston, MA, USA: Springer, 2011, pp. 1–35.
  3. G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the stateof-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, 2005.
  4. M. de Gemmis, P. Lops, C. Musto, F. Narducci, and G. Semeraro, Semantics-aware content-based recommender systems, in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, eds. Boston, MA, USA: Springer, 2015, pp. 119–159.
  5. S. Loh, F. Lorenzi, R. Salda˜na, and D. Lichtnow, A tourism recommender system based on collaboration and text analysis, Information Technology & Tourism, vol. 6, no. 3, pp. 157–165, 2003.
  6. D. Gavalas, C. Konstantopoulos, K. Mastakas, and G. Pantziou, Mobile recommender systems in tourism, Journal of Network and Computer Applications, vol. 39, pp. 319–333, 2014.
  7. K. N. Rao and V. G. Talwar, Application domain and functional classification of recommender systems—A survey, DESIDOC Journal of Library & Information Technology, vol. 28, no. 3, pp. 17–35, 2008.
  8. X. Y. Su and T. M. Khoshgoftaar, A survey of collaborative filtering techniques, Adv. Artif. Intell., vol. 2009, p. 421425, 2009.
  9. I. Cenamor, T. de la Rosa, S. N´u˜nez, and D. Borrajo, Planning for tourism routes using social networks, Expert Syst. Appl., vol. 69, pp. 1–9, 2017.
  10. G. Fenza, E. Fischetti, D. Furno, and V. Loia, A hybrid context aware system for tourist guidance based on collaborative filtering, in Proc. IEEE Int. Conf. Fuzzy Systems, Taipei, China, 2011, pp. 131–138.
  11. K. Meehan, T. Lunney, K. Curran, and A. McCaughey, Context-aware intelligent recommendation system for tourism, presented at 2013 IEEE Int. Conf. Pervasive Computing and Communications Workshops (PERCOM Workshops), San Diego, CA, USA, 2013, pp. 328–331.
  12. O. Boulaalam, B. Aghoutane, D. El Ouadghiri, A. Moumen, and M. L. C. Malinine, Proposal of a big data system based on the recommendation and profiling techniques for an intelligent management of moroccan tourism, Procedia Computer Science, vol. 134, pp. 346–351, 2018.
  13. J. Borr`as, A. Moreno, and A. Valls, Intelligent tourism recommender systems: A survey, Expert Systems with Applications, vol. 41, no. 16, pp. 7370–7389, 2014.
  14. L. Ravi and S. Vairavasundaram, A collaborative location based travel recommendation system through enhanced rating prediction for the group of users, Computational Intelligence and Neuroscience, vol. 2016, p. 1291358, 2016.
  15. P. Di Bitonto, M. Laterza, T. Roselli, and V. Rossano, A recommendation system to promote local cultural heritage, Journal of E-Learning and Knowledge Society, vol. 7, no. 3, pp. 97–107, 2011.
  16. Z. Aarab, A. Elghazi, R. Saidi, and M. D. Rahmani, Toward a smart tourism recommender system: Applied to tangier city, in Innovations in Smart Cities and Applications, M. Ben Ahmed and A. Boudhir, eds. Cham, Switzerland: Springer, 2018, pp. 643–651.
  17. J. P. Lucas, N. Luz, M. N. Moreno, R. Anacleto, A. A. Figueiredo, and C. Martins, A hybrid recommendation approach for a tourism system, Expert Systems with Applications, vol. 40, no. 9, pp. 3532–3550, 2013.
  18. M. Hong, J. J. Jung, F. Piccialli, and A. Chianese, Social recommendation service for cultural heritage, Personal and Ubiquitous Computing, vol. 21, no. 2, pp. 191–201, 2017.
  19. M. Vozalis and K. G. Margaritis, On the enhancement of collaborative filtering by demographic data, Web Intelligence and Agent Systems, vol. 4, no. 2, pp. 117–138, 2006.
  20. A. T. Nguyen, N. Denos, and C. Berrut, Exploitation des donn´ees ”disponibles `a froid” pour am´eliorer le d´emarrage a froid dans les syst`emes de filtrage d’information, in Actes du XXIV Congr`es d’INFORSID, Hammamet, Tunisie, 2006,pp. 81–95.

International Journal of Scientific Research in Science and Technology

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Published

2020-06-30

Issue

Volume 7, Issue 3, May-June-2020

Section

Research Articles

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How to Cite

[1]
M. Venu Gopalachari, A Mohan, B S S P Kumar, " A Conceptual Model of Hybrid Recommender using Big Data and Machine Learning Approach, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 7, Issue 3, pp.479-484, May-June-2020.