A Personalized Tour Recommender in Python using Decision Tree

8 Pages Posted: 11 Jul 2023

See all articles by Nayma Khan

Nayma Khan

Integral University

Mohammad Haroon

Integral University

Date Written: June 15, 2023

Abstract

A tourist recommendation system has been implemented based on python, Django framework and MySQL database. Firstly, crawler technology is used to crawl the ratings(reviews of customer experiences) information of TripAdvisor, and then the crawled ratings data is stored in MySQL. In the system, users can view the location, can view the opinion analytics(review packages), and collect the location information. According to the user's collection of location information, the decision tree is used to recommend locations that may be of interest to users. If a new user enter his requirements then decision tree will predict best location based on his given input. Decision tree don’t need new users past experience data. Through the design of these functional modules, the whole tourist recommendation system is realized.

Keywords: Recommendation System, K - Nearest Neighbor (KNN) Algorithm, Convolution Neural Network (CNN) Algorithm, Decision Tree Algorithm

Suggested Citation

Khan, Nayma and Haroon, Mohammad, A Personalized Tour Recommender in Python using Decision Tree (June 15, 2023). Available at SSRN: https://ssrn.com/abstract=4493066 or http://dx.doi.org/10.2139/ssrn.4493066

Nayma Khan (Contact Author)

Integral University ( email )

Mohammad Haroon

Integral University

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
71
Abstract Views
304
Rank
601,650
PlumX Metrics