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http://dx.doi.org/10.30693/SMJ.2020.9.1.45

Sequence-Based Travel Route Recommendation Systems Using Deep Learning - A Case of Jeju Island -  

Lee, Hee Jun (계명대학교 경영정보학과)
Lee, Won Sok (계명대학교 경영정보학과)
Choi, In Hyeok (계명대학교 경영정보학과)
Lee, Choong Kwon (계명대학교 경영대학 경영정보학과)
Publication Information
Smart Media Journal / v.9, no.1, 2020 , pp. 45-50 More about this Journal
Abstract
With the development of deep learning, studies using artificial neural networks based on deep learning in recommendation systems are being actively conducted. Especially, the recommendation system based on RNN (Recurrent Neural Network) shows good performance because it considers the sequential characteristics of data. This study proposes a travel route recommendation system using GRU(Gated Recurrent Unit) and Session-based Parallel Mini-batch which are RNN-based algorithm. This study improved the recommendation performance through an ensemble of top1 and bpr(Bayesian personalized ranking) error functions. In addition, it was confirmed that the RNN-based recommendation system considering the sequential characteristics in the data makes a recommendation reflecting the meaning of the travel destination inherent in the travel route.
Keywords
Deep learning; RNN(Recurrent Neural Network); Recommendation systems; Jeju Island;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 이정민, "지역분권화에 따른 지역관광 발전 방안," 한국관광정책, 제73호, 40-49쪽, 2018
2 B. Hidasi, A. Karatzoglou, L. Baltrunas, and D. Tikk, "Session-based recommendations with recurrent neural networks," arXiv preprint arXiv:1511.06939. 2015.
3 안병익, 정구임 , 최혜림. "다속성 태도 모델과 협업적 필터링 기반 장소 추천 연구," 스마트미디어저널, 제5권, 제2호, 1-6쪽, 2016
4 홍택은, 신주현. "이미지와 텍스트 정보의 카테고리 분류에 의한 SNS 팔로잉 추천 방법," 스마트미디어저널, 제5권, 제3호, 1-8쪽, 2016
5 Y. Zheng, L. Zhang, X. Xie, and W.Y. Ma, "Mining interesting locations and travel sequences from GPS trajectories," In Proceedings of the 18th international conference on World wideweb, ACM, pp. 791-800, 2009.
6 Z. Yu, H. Xu, Z. Yang, and B. Guo, "Personalized travel package with multi-point-of-interest recommendation based on crowdsourced user footprints," IEEE Transactions on Human-Machine Systems, vol. 46, no. 1, pp. 151-158, 2016.   DOI
7 T. Kurashima, T. Iwata, G. Irie, and K. Fujimura, "Travel route recommendation using geotags in photo sharing sites," Proc. of the 19th ACM international conference on Information and knowledge management, ACM, pp. 579-588. 2010.
8 H.C. Kum, J.H. Chang, and W. Wang, "Sequential Pattern Mining in Multi- Databases via Multiple Alignment," Data Mining and Knowledge Discovery, vol. 12, no. 2, pp. 151-180, 2006.   DOI
9 Ji, X., J. Bailey. and G. Dong., "Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints," Knowledge and Information Systems, vol. 11, no. 3, pp. 259-296, 2007.   DOI
10 U. Yun, "A New Framework for Detecting Weighted Sequential Patterns in Large Sequence Databases," Knowledge-Based Systems, vol. 21, no. 2, pp. 110-122, 2008.
11 장중혁, "발생 간격 기반 가중치 부여 기법을 활용한 데이터 스트림에서 가중치 순차패턴 탐색," 지능정보연구, 제16권, 제3호, 55-75쪽, 2010
12 이태석, 강승식, "LSTM 기반의 sequence-to-sequence 모델을 이용한 한글 자동띄어쓰기," 스마트미디어저널, 제7권, 제4호, 17-23쪽, 2018   DOI
13 Clements, M., Serdyukov, P., De Vries, A. P. and Reinders, M. J. "Using flickr geotags to predict user travel behaviour." Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 2010.
14 Yoon, H., Zheng, Y., Xie, X. and Woo, W. "Social itinerary recommendation from user-generated digital trails." Personal and Ubiquitous Computing, vol. 16, no. 5, pp. 469-484, 2012.   DOI