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http://dx.doi.org/10.14400/JDC.2021.19.5.187

Implementation of a Personalized Restaurant Recommendation System for The Mobility Handicapped  

Lee, Jin-Ju (School of Business, Yeungnam University)
Park, So-Yeon (School of Information and Communication Engineering, Yeungnam University)
Kim, Seo-Yun (School of Statistics, Yeungnam University)
Lee, Jeong-Eun (Department of Statistics, Kyungpook University)
Kim, Keun-Wook (Big Data Center, Daegu Digital Industry Promotion Agency)
Publication Information
Journal of Digital Convergence / v.19, no.5, 2021 , pp. 187-196 More about this Journal
Abstract
The mobility handicapped are representative socially vulnerable people who account for a high percentage of our society. Due to the recent development of technology, personalized welfare technologies for the socially vulnerable are being studied, but it is relatively insufficient compared to the general people. In this study, we intend to implement a personalized restaurant recommendation system for the mobility handicapped. To this end, a hybrid recommendation system was implemented by combining the data of special transportation boarding and alighting history (7,153 cases) and information of Daegu Food restaurants (955 cases). In order to evaluate the effectiveness of the implemented recommendation system, we conducted performance comparisons with existing recommendation systems by prediction error rate and recommendation coverage. As a result of the analysis, the performance was higher than that of the existing recommendation system, and the possibility of a personalized restaurant recommendation system for the mobility handicapped was confirmed. In addition, we also confirmed the correlation in which similar restaurants are recommended in some types of the mobility handicapped. As a result of this study, it is judged that it will contribute to the use of restaurants with high satisfaction for the mobility handicapped, and the limitations of the study are also presented.
Keywords
The mobility handicapped; STS; Restaurant recommendation; Recommendation system; Hybrid recommendation system;
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  • Reference
1 MOLIT (2019). A Study on the Actual Condition of travel convenience for the mobility handicapped, Sejong.
2 W. H. Kim, S. H. Lee & S. H. Kim. (2008). A Study on Travel Behavior of the Mobility Handicapped and Custom-made Transit Information System. Seoul Studies, 9(2), 105-119.
3 M. K. Jang, W. T. Lim, K. S. Kim & M. K. Moon. (2013). Customized Navigation System for Walking Safety of the Transportation Vulnerable. Journal of the Korean Institute of Next Generation Computing, 9(5), 17-26.
4 B. M. Park & E. J. Shim. (2013). Barrier Free Design Methods applied in Passenger Terminals based on Characteristics of Transportation Poor & Barrier Free Elements - Focused on the Gunsan International*Coastal Passenger Boat Terminal -. Journal of the Korean Institute of Interior Design, 22(5), 344-356. DOI : 10.14774/JKIID.2013.22.5.344   DOI
5 S. W. Chung & J. H. Rho. (2017). Case Study of Barrier Free Design for Transportation Vulnerable: Focusing on Transfer Station Complex in Seoul Station. Journal of the Korea Academia-Industrial cooperation Society. 18(3), 333-344 DOI : 10.5762/KAIS.2017.18.3.333   DOI
6 S. S. Heo, Y. K. Choi & Y. H. Park. (2018). Design and Implementation of Low-Floor Bus Reservation System for the Transportation Weak. Journal of the Korea Industrial Information Systems Research, 23(6), 39-46. DOI : 10.9723/jksiis..2018.23.6.039   DOI
7 J. H. Kim, B. H. Ahn & D. Y. Jeong (2012). A Recommender System using Mixed Filtering for Health Products. The Journal of Internet Electronic Commerce Research, 12(2), 109-124.
8 J. E. Son, S. B. Kim, H. J. Kim & S. Z. Cho. (2015). Review and Analysis of Recommender Systems. Journal of the Korean, 41(2), 185-208. DOI : 10.7232/JKIIE.2015.41.2.185   DOI
9 K. W. Kim, S. H. Son, M. Y. Yang & S. H. Lee. (2020). Frequency of Special Transportation Estimation Model Using Deep Learning(Nadri Call). Korean Society of Transportation, 17(2), 43-51.
10 K. W. Kim, B. M. Koo, J. H. Si & H. H. Jeon. (2020). Location Analysis of Charging Stations for the Disabled Person using Big Data. Korean Society of Transportation. 17(5), 7-16.
11 J. M. Ko & D. H. Nam. (2011). Development of Hybrid Filtering Recommendation System using Context-Information in Mobile Environments. The Journal of The Korea Institute of Intelligent Transport Systems, 10(3), 95-100.
12 D. S. Kim. (2016). User-Customized restaurant recommender system based on Collective Intelligence. Master's thesis. Kangwon National University, Gangwonl.
13 H. S. Choi, Q. Peng & W. S. Rhee. (2020). Design and Implementation of the Machine Learning-based Restaurant Recommendation System. Journal of Digital Contents Society, 21(2), 259-268. DOI : 10.9728/dcs.2020.21.2.259   DOI
14 G. Geetha, M. Safa, C. Fancy & D. Saranya. (2018). A hybrid approach using collaborative filtering and content based filtering for recommender system. Journal of Physics Conference Series, 1000(1), 012101. DOI : 10.1088/1742-6596/1000/1/012101   DOI
15 E. Y. Bae & S. J. Yu. (2018). Keyword-based Recommender System Dataset Construction and Analysis. Journal of KIIT, 16(6), 91-99. DOI : 0.14801/jkiit.2018.16
16 R. V. Meteren & M. V. Someren. (2000). Using content-based filtering for recommendation. Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, 30, 47-56.
17 B. Sarwar, G. Karypis, J. Konstan & J. Riedl. (2001). Item-based collaborative filtering recommendation algorithms. WWW '01: Proceedings of the 10th international conference on World Wide Web, 285-295. DOI : 10.1145/371920.372071   DOI
18 R. Burke. (2002). Hybrid recommender systems : survey and experiments. User Modeling and User-Adapted Interaction, 12, 331-370. DOI : 10.1023/A:1021240730564   DOI
19 B. I. Ahn, K. I. Jung & H. L. Choi. (2017). Mobile Context Based User Behavior Pattern Inference and Restaurant Recommendation Model. Journal of Digital Contents Society, 18(3), 535-542. DOI : 10.9728/dcs.2017.18.3.535   DOI
20 K. W. Kim, D. S. Yun & J. J. Kim. (2020). Travel Demand Analysis of Special Transportation Systems for the Transportation Vulnerable using Big Data: A Case Study of Daegu Metropolitan City. Journal of Daegu Gyeongbuk Studies. 19(2), 43-61.   DOI