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LAPSE:2018.0437
Published Article
LAPSE:2018.0437
Design and Implementation of an Optimal Travel Route Recommender System on Big Data for Tourists in Jeju
Lei Hang, Sang-Hun Kang, Wenquan Jin, Do-Hyeun Kim
August 28, 2018
A recommender system is currently applied in many different domains, seeking to provide users with recommendation services according to their personalized preferences to relieve rising online information congestion. As the number of mobile phone users is large and growing, mobile tourist guides have attracted considerable research interest in recent years. In this paper, we propose an optimal travel route recommender system by analyzing the data history of previous users. The open dataset used covers the travel data from thousands of mobile tourists who visited Jeju in a full year. Our approach is not only personalized to users’ preferences but also able to recommend a travel route rather than individual POIs (Points of Interest). An association rule mining-based approach, which takes into account contextual information (date, season and places already visited by previous users), is used to produce travel routes from the large dataset. Furthermore, to ensure the reasonability of the recommendation, a genetic algorithm optimization approach is proposed to find the optimal route among them. Finally, a mobile tourist case study is implemented in order to verify the feasibility and applicability of the proposed system. This application embeds a graphic map for plotting the travel route and provides detailed information of each travel spot as well. The results of this work indicate that the proposed system has great potential for travel planning preparation for mobile users.
Keywords
association rule mining, mobile, route optimization, travel planning, travel route recommendation
Suggested Citation
Hang L, Kang SH, Jin W, Kim DH. Design and Implementation of an Optimal Travel Route Recommender System on Big Data for Tourists in Jeju. (2018). LAPSE:2018.0437
Author Affiliations
Hang L: Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
Kang SH: Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
Jin W: Department of Computer Engineering, Jeju National University, Jeju 63243, Korea [ORCID]
Kim DH: Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
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Journal Name
Processes
Volume
6
Issue
8
Article Number
E133
Year
2018
Publication Date
2018-08-17
Published Version
ISSN
2227-9717
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PII: pr6080133, Publication Type: Journal Article
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LAPSE:2018.0437
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doi:10.3390/pr6080133
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Aug 28, 2018
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