Open AccessPrediction of Top Tourist Attraction Spots using Learning Algorithms
Author(s) -
Sagar Gupta*,
Jenila Livingston,
Agnel Livingston L.G.X.
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c4241.098319
Subject(s) - tourism , computer science , attraction , social media , kernel density estimation , task (project management) , tourist attraction , recommender system , identification (biology) , kernel (algebra) , random forest , data science , machine learning , algorithm , data mining , artificial intelligence , world wide web , mathematics , geography , engineering , philosophy , linguistics , statistics , botany , archaeology , systems engineering , combinatorics , estimator , biology
Dealing with the growing amount of user posted content like preferences, responses, comments, past experiences and beliefs spread through social media is a vital but challenging task. Being applied in several domains, recommender systems are used to find solutions and suggestions based on users interests including tourism-related opinion detection and tourist-attraction spot identification. Tourists can access and analyze this information for making decisions and predicting best tourist places. This study aims to predict tourist attraction spots and their related information by analyzing the data from social media (Facebook, Twitter etc.) which in turns help the tourist industry by deliberating what kind of attractions tourists can have and how to obtain their preferences. For this purpose four algorithms such as Kernel Density Estimation, K- Nearest Neighbor, Random forest and XG Boost have been used. The findings revealed that XG Boost yields better results in terms of accuracy than other three algorithms.