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.

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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

Vol. 4 No. 04 (2015): April

Section

Original Research Article

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Copyright (c) 2015 COMPUSOFT: An International Journal of Advanced Computer Technology

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