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http://dx.doi.org/10.15207/JKCS.2020.11.8.023

Design and Implementation of Place Recommendation System based on Collaborative Filtering using Living Index  

Lee, Ju-Oh (Department of Computer Engineering, Sejong University)
Lee, Hyung-Geol (Department of Computer Engineering, Sejong University)
Kim, Ah-Yeon (Department of Computer Engineering, Sejong University)
Heo, Seung-Yeon (Department of Computer Engineering, Sejong University)
Park, Woo-Jin (Department of Computer Engineering, Sejong University)
Ahn, Yong-Hak (Department of Computer Engineering, Sejong University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.8, 2020 , pp. 23-31 More about this Journal
Abstract
The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.
Keywords
Convergence; Collaborative Filtering; Recommendation System; Living Index; Personalization Service;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 H. J. Bae & S. W. Lee. (2020). A study on user recognition of personalized recommended service platforms by content characteristics. Journal of Korea Broadcasting and Telecommunication Studies, 34(3), 5-42.
2 G. S. Ko et al. (2017). Contents Recommendation Scheme Considering User Activity in Social Network Environments. Journal of The Korea Contents Association, 17(2), 404-414.   DOI
3 S. H. Park, J. W. Kim, D. H. Kim & H. J. Cho. (2019). Music Therapy Counseling Recommendation Model Based on Collaborative Filtering. Journal of the Korea Convergence Society, 10(9), 31-36. DOI: 10.15207/JKCS.2019.10.9.031
4 S. J. Lee, T. R. Jeon, G. D. Baek & S. S. Kim. (2009). A Movie Rating Prediction System of User Propensity Analysis based on Collaborative Filtering and Fuzzy System. Journal of Korean Institute of Intelligent Systems, 19(2), 242-247.   DOI
5 K. I. Jung, B. I Ahn, J. J. Kim & K. J. Han. (2014). Location Recommendation System based on LBSNS. Journal of Digital Convergence, 12(6), 277-287. DOI: 10.14400/JDC.2014.12.6.277   DOI
6 C. Y. Cho, G. I. Jung, Y. M. Seo & H. R. Choi. (2017). An Empirical Study on the Influence of Weather and Daytime on Restaurant Menu search System. Smart media journal, 6(2), 50-56.
7 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), 535.0-542.0.   DOI
8 J. W. Roh, K. H. Yoon, J. K. Kim & J. H. Lee. (2008). A Music Recommendation System Using Collaborative Filtering and Context Awareness. Conference of Korean Society of Intelligent Systems, 18(2), 76-79.
9 J. E. Son, S. B. Kim, H. J. Kim & S. Z. Cho. (2015). Review and Analysis of Recommender Systems. Journal of Korean institute of industrial engineers, 41(2), 185.0-208.0.   DOI
10 J. Byun & D. K. Kim. (2016). Design and Implementation of Location Recommending Services using Personal Emotional Information based on Collaborative Filtering. Journal of the Korea Institute of Information and Communication Engineering, 20(8), 1407-1414.   DOI
11 H. C. Shin & S. B. Cho. (2013). A Location-based Collaborative Filtering Recommender using Quadtree. Journal of KIISE : Computing Practices and Letters, 19(1), 15-22.
12 Y. S. Yoo, J. S. Kim, B. Y. Sohn & J. J. Jung. (2017). Evaluation of Collaborative Filtering Methods for Developing Online Music Contents Recommendation System. The transactions of the Korean Institute of Electrical Engineers, 66(7), 1083.0-1091.0.   DOI
13 H. J. Kwon & K. S. Hong. (2010). Personalization of LBS using Recommender Systems Based on Collaborative Filtering. Journal of Internet Computing and Services (JICS), 11(6), 1-11.
14 S. H. Bae, T. Y. Kim & D. W. Seo. (2017). Design of Emotion Image Recommendation System using Bio Emotion Information and Collaborative Filtering. Journal of advanced engineering and technology, 10(4), 479-487.   DOI
15 J. M. Kim. (2018). Study on the Development of Collaborative Filtering Systems and Its Application. Journal of Social Science, 29(2), 197-209.   DOI
16 K. J. Hyun, J. W. Park & H. S Choi. (2015). The Effect of Customers' Experience of Diverse Goods and Selection of Popular Commodity on Recommendation System. Journal of The Korean Data Analysis Society, 17(6), 3097-3106.
17 J. L. Herlocker, J. A. Konstan, Al Borchers & J. Riedl. (1999). An Algorithmic Framework for Performing Collaborative Filtering, In Proceeding of SIGIR-99, 230-237.