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http://dx.doi.org/10.9717/kmms.2019.22.9.1108

A Development of Optimal Travel Course Recommendation System based on Altered TSP and Elasticsearch Algorithm  

Kim, Jun-Yeong (Dept. of Multimedia Engineering, Sunchon National University)
Jo, Kyeong-Ho (Dept. of Multimedia Engineering, Sunchon National University)
Park, Jun (Dept. of Multimedia Engineering, Sunchon National University)
Jung, Se-Hoon (School of Connection(Bigdata Convergence), Youngsan University)
Sim, Chun-Bo (Dept. of Multimedia Engineering, Sunchon National University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.9, 2019 , pp. 1108-1121 More about this Journal
Abstract
As the quality and level of life rise, many people are doing search for various pieces of information about tourism. In addition, users prefer the search methods reflecting individual opinions such as SNS and blogs to the official websites of tourist destination. Many of previous studies focused on a recommendation system for tourist courses based on the GPS information and past travel records of users, but such a system was not capable of recommending the latest tourist trends. This study thus set out to collect and analyze the latest SNS data to recommend tourist destination of high interest among users. It also aimed to propose an altered TSP algorithm to recommend the optimal routes to the recommended destination within an area and a system to recommend the optimal tourist courses by applying the Elasticsearch engine. The altered TSP algorithm proposed in the study used the location information of users instead of Dijkstra's algorithm technique used in previous studies to select a certain tourist destination and allowed users to check the recommended courses for the entire tourist destination within an area, thus offering more diverse tourist destination recommendations than previous studies.
Keywords
Travel; Recommend System; SNS; Crawling; Reinforcement TSP; Elasticsearch;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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