Title: Smart personalised recommendation system for wanderer using prediction analysis

Authors: L. Maria Michael Visuwasam; M. Geetha; G. Gayathri; K. Divya; D. Elakkiya

Addresses: Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India ' Department of Computer Science and Engineering, Rajalakshmi Institute of Technology, Chennai, Tamil Nadu, India

Abstract: Indian tourism is one of the rapid growing industries that play an important role in country economies. It is not easy to get smart suggestions for place and accommodation to personalised travel itineraries viz. individual and group tourist based on the interest preference. The proposed system aims to provide output based on user point of interest in tourism. Extraction of opinions from user reviews, specific to accommodation services, are useful to the clients looking for accommodation. The proposed system extracts the famous place reviews using the tags and comments from Flicker. The dataset is then classified using the POI algorithm technique. The recommendation system also helps to solve problems by providing the budget estimation using the cost estimation algorithm in machine learning. Using the application, the user can also visit many unknown places on their route to destination. It will be an optimised itinerary recommender that makes the user to save the time and enjoy their vacation.

Keywords: point of interest; POI; algorithm; machine learning; cost estimation; map API; weather forecasting; seasonal extraction; recommendation system.

DOI: 10.1504/IJISC.2021.119078

International Journal of Intelligence and Sustainable Computing, 2021 Vol.1 No.3, pp.223 - 232

Received: 01 Apr 2020
Accepted: 12 Jun 2020
Published online: 22 Nov 2021 *

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