Machine Learning Approach for Automatic Seasonal Tour Package

Authors

  • Biswamayee Research Scholar, Department of CSE, Bharath University, Chennai
  • Komal Research Scholar, Department of CSE, Bharath University, Chennai
  • Pothumani Assistant professor, Department of CSE, Bharath University, Chennai

Keywords:

Tourist relational area season topic (TRAST), TAST

Abstract

The online travel data imposes associate increasing difficult for tourists United Nations agency need to choose between sizable amount of accessible package for satisfying their customized desires. This TAST model will represent travel packages and tourists by totally different topics distribution, wherever the topics extraction is conditioned on each the tourists and also the intrinsic options like location , travel seasons of the landscapes. supported this subject model illustration we have a tendency to planned a cocktail approaches to come up with the list for customized travel package recommendation. we have a tendency to extend the TAST model to the tourist-relation-area-season topic (TRAST)model for capturing the latent relationships among the tourists in every travel cluster.

References

. A. Jameson and B. Smyth, “Recommendation to Groups,” The Adaptive Web, vol. 4321, pp. 596-627, 2007.

. B.D. Carolis, N. Novielli, V.L. Plantamura, and E. Gentile, “Generating Comparative Descriptions of Places of Interest in the Tourism Domain,” Proc. Third ACM Conf. Recommender Systems . (RecSys ‟09), pp. 277-280, 2009.

. C. Wang and D. Blei, “Collaborative Topic Modeling Recommending Scientific Articles,” Proc. ACM 17th ACM SIGKDD

. Int‟l Conf. Knowledge Discovery and Data Mining, pp. 448-456, 2011.

. D. Agarwal and B. Chen, “fLDA: Matrix Factorization through Latent Dirichlet Allocation,” Proc. Third ACM Int‟l Conf. Web Search and Data Mining (WSDM 10), pp. 91-100, 2010.

. D.M. Blei, Y.N. Andrew, and I.J. Michael, “Latent Dirichlet Allocation,” J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.

. F. Cena et al., “Integrating Heterogeneous Adaptation Techniques to Build a Flexible and Usable Mobile Tourist Guide,” AI Comm., vol.

, no. 4, pp. 369-384, 2006.

. G.D. Abowd et al., “Cyber-Guide: A Mobile Context-Aware Tour Guide,” Wireless Networks, vol. 3, no. 5, pp. 421-433, 1997.

. G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible

Extensions,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.

. N.A.C. Cressie, Statistics for Spatial Data. Wiley and Sons, 1991.

. O. Averjanova, F. Ricci, and Q.N. Nguyen, “Map-Based Interaction with a Conversational Mobile Recommender System,” Proc. Second Int‟l Conf. Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM ‟08), pp. 212-218, 2008.

Downloads

Published

2024-02-26

How to Cite

Biswamayee, Komal, & Pothumani, S. (2024). Machine Learning Approach for Automatic Seasonal Tour Package. COMPUSOFT: An International Journal of Advanced Computer Technology, 4(04), 1609–1612. Retrieved from https://ijact.in/index.php/j/article/view/281

Issue

Section

Original Research Article

Similar Articles

1 2 3 4 5 6 7 > >> 

You may also start an advanced similarity search for this article.