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http://dx.doi.org/10.9728/dcs.2018.19.5.871

A Study on Correlation Analysis and Preference Prediction for Point-of-Interest Recommendation  

Park, So-Hyun (Department of IT Engineering, Sookmyung Women's University)
Park, Young-Ho (Department of IT Engineering, Sookmyung Women's University)
Park, Eun-Young (Department of Visual Design, Hyupsung University)
Ihm, Sun-Young (Department of Big Data Research Center, Sookmyung Women's University)
Publication Information
Journal of Digital Contents Society / v.19, no.5, 2018 , pp. 871-880 More about this Journal
Abstract
Recently, the technology of recommendation of POI (Point of Interest) related technology is getting attention with the increase of big data related to consumers. Previous studies on POI recommendation systems have been limited to specific data sets. The problem is that if the study is carried out with this particular dataset, it may be suitable for the particular dataset. Therefore, this study analyzes the similarity and correlation between stores using the user visit data obtained from the integrated sensor installed in Seoul and Songjeong roads. Based on the results of the analysis, we study the preference prediction system which recommends the stores that new users are interested in. As a result of the experiment, various similarity and correlation analysis were carried out to obtain a list of relevant stores and a list of stores with low relevance. In addition, we performed a comparative experiment on the preference prediction accuracy under various conditions. As a result, it was confirmed that the jacquard similarity based item collaboration filtering method has higher accuracy than other methods.
Keywords
POI Recommendation; Correlation Analysis; Item-based Collaborative Filtering; Predictive System; Recommendation Algorithm;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 G. N. Lee, "Design of a Personalized POI Recommendation System in Location Based Social Networks", M.S. dissertation, Chungbuk National University, 2018.
2 Y. H. Choi, S. Y. Lee, "Users' Moving Patterns Analysis for Personalized Product Recommendation in Offline Shopping Malls", Domestic Journal of Korean Institute of Intelligent Systems, Vol.16, No.2, pp. 185-190, 2006.   DOI
3 D. H. Kim, J. M. Kim, S. W. Park, "Decision Tree Based Application Recommendation System," in Proceedings of Korean Institute of Information Scientists and Engineers, Vol.39, No.1(D), pp. 140-142, 2012.
4 W. Luan, G. Liu, C. Jiang, "Partition-based Collaborative Tensor Factorization for POI Recommendation," International Journal of Automatica Sinica, Vol.4, No.3, pp.437-446, 2017.   DOI
5 P. Zhao, X. Xu, Z. Zhou, K. Zheng, V. S. Sheng, H. Xiong, "Exploiting Hierarchical Structures for POI Recommendation", 2017 IEEE International Conference on Data Mining (ICDM), pp.655-664, 2017.
6 M. J. Kim, S. J. Lee, "Measures of Abnormal User Activities in Online Comments Based on Cosine Similarity," Domestic Journal of The Korea Institute of Information Security and Cryptology, Vol.24, No.2, pp. 335-343, 2014.   DOI
7 L. C. Han, "Quantitative Measure of the Changes of Migration Patterns Using Cosine Similarity," Domestic Journal of Korean Society of Rural Planning, Vol.23, No.2, pp. 67-74, 2017.   DOI
8 S. H. Kim, J. G. Park, S. Y. Han, "A Framework to Evaluate Communication Quality of Operators in Nuclear Power Plants Using Cosine Similarity," Journal of The Korea Society of Computer and Information, Vol.15, No.9, pp. 165-172, 2010.   DOI
9 Building recommendation engines : understand your data and user preferences to make intelligent, accurate, and profitable decisions, S. Gorakala, Packt Publishing
10 R-Probability and Statistics, T. J. Lim, Life and Power Press
11 Seoullo, http://seoullo7017.seoul.go.kr
12 Naver maps, https://map.naver.com
13 S. N. Lee, H. M. Kim, C. G. Lee, G. S. Lee, "Study on Automatic Bug Triage using Deep Learning)", Domestic Journal of Korean Institute of Information Scientists and Engineers, Vol.44, No.11, pp.1156-1164, 2017.