한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제25권9호
  • /
  • Pages.53-62
  • /
  • 2020
  • /
  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
DOI QR코드

DOI QR Code

Digital Signage service through Customer Behavior pattern analysis

  • Shin, Min-Chan (Dept. of Computer Science, Hoseo University) ;
  • Park, Jun-Hee (Dept. of Computer Science, Hoseo University) ;
  • Lee, Ji-Hoon (Dept. of Computer Science, Hoseo University) ;
  • Moon, Nammee (Division of Computer and Information Engineering, Hoseo University)
  • 투고 : 2020.08.13
  • 심사 : 2020.09.09
  • 발행 : 2020.09.29
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 연구된 상품 추천 서비스들은 고객들의 구매 이력을 통해서만 추천이 이루어졌다. 본 논문에서는 구매 이력을 통해 추천뿐만 아니라 고객이 상품을 고를 때 취하는 행동 패턴을 분석하여 관심도가 높은 광고를 노출하는 행동 패턴 분석 기반 디지털 사이니지 서비스를 제안한다. 이 서비스는 고객행동 패턴을 분석하여 실질적으로 관심을 가지는 상품에 대해 관심도를 추출한다. 추출된 관심도와 고객들의 구매 이력을 Wide & Deep 모델을 통해 학습하고, 이를 바탕으로 MF(Matrix Factorization) 모델을 통해 다른 상품들의 희소 벡터를 예측한다. 예측된 상품 관심도에 대한 순위를 도출하고, 적합한 광고를 노출하기 위해 고객과 상호 작용할 수 있는 인도어 사이니지를 활용한다. 본 논문의 서비스를 통해 온라인뿐만이 아닌 오프라인 환경에서도 고객의 관심 정보를 파악하고 단순히 무작위로 노출하는 광고가 아닌 고객에게 적합한 광고를 제공하여 만족도 높은 구매 환경이 조성될 것이다.

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers' behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers' purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers' interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

키워드

참고문헌

  1. S. Chang, and D. Youm, "The Effects of Media Creativity and Interactivity of Indoor Digital Signage on the Attitude," Journal of Outdoor Advertising Research, Vol.16, No.4, pp.5-23, November 2019.
  2. S. Yoo, and M. Jung, "The Effects of In-store Augmented Reality Virtual Fitting Digital Signage on Shoppers : Focusing on VMD Production Components and Types of Advertised Product," The Korean Journal of Advertising and Public Relations, Vol.21, No.4, pp.135-167, October 2019. https://doi.org/10.16914/kjapr.2019.21.4.135
  3. J. Lee, K. Ae, and J. Ryu, "Development of Hand Recognition Interface for Interactive Digital Signage," Journal of the Korea Industrial Information Systems Research, Vol.22, No.3, pp.1-11, June 2017, DOI: 10.9723/jksiis.2017.22.3.001
  4. C. Kim, C. Jo, and J. Jeong, "A Recommendation Technique Based on Offline Product Using Similarity," Journal of Knowledge Information Technology and Systems, Vol.14, No.4, pp.335-344, August 2019, DOI: 10.34163/jkits.2019.14.4.003
  5. J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, "Collaborative filtering and deep learning based recommendation system for cold start items," Expert Systems with Applications, Vol.69, pp.29-39, March 2017, DOI: 10.1016/j.eswa.2016.09.040
  6. H. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X, Liu, and H. Shah, "Wide & Deep learning for recommender systems," In Proceedings of the 1st workshop on deep learning for recommender systems, pp.7-10, September 2016. DOI: 10.1145/2988450.2988454
  7. A. Imran, M. Amin, and F. Johora, "Classification of Chronic Kidney Disease using Logistic Regression, Feedforward Neural Network and Wide & Deep Learning," In 2018 International Conference on Innovation in Engineering and Technology (ICIET), pp.27-29, December 2018. DOI: 10.1109/CIET.2018.8660844
  8. P. Covington, J. Adams, and E. Sargin, "Deep neural networks for youtube recommendations," In Proceedings of the 10th ACM conference on recommender systems, pp.191-198, September 2016. DOI: 10.1145/2959100.2959190
  9. M. Kim, S. Lee, and J. Kim, "A Wide & Deep Learning Sharing Input Data for Regression Analysis," In 2020 IEEE International Conference on Big Data and Smart Computing (BigComp), pp.8-12, February 2020. DOI: 10.1109/BigComp48618.2020.0-108
  10. I. Al-Hadi, M. Sharef, N. Sulaiman, and N. Mustapha, "Review of the temporal recommendation system with matrix factorization," Int. J. Innov. Comput. Inf. Control, Vol.13, No.5, pp.1579-1594, October 2017.
  11. D. Agarwal, and C. Chen, "Regression-based latent factor models," In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.19-28, June 2019. DOI: 10.1145/1557019.1557029
  12. Y. Yu, C. Wang, H. Wang and Y. Gao, "Attributes coupling based matrix factorization for item recommendation," Applied Intelligence, Vol.46, No.3, pp.521-533, April 2017. DOI: 10.1007/s10489-016-0841-8
  13. B. Ahn, K. Jung, and H. Choi, "Mobile Context Based User Behavior Pattern Inference and Restaurant Recommendation Model," Journal of Digital Contents Society, Vol.18, No.3, pp.535-542, May 2017. DOI: 10.9728/dcs.2017.18.3.535
  14. Q. Li, and D. Liu, "Research of music recommendation system based on user behavior analysis and word2vec user emotion extraction," International Conference on Intelligent and Interactive Systems and Applications, Vol.686, pp.469-475, November 2017. DOI: 10.1007/978-3-319-69096-4_65
  15. Z. Fang, L. Zhang, and K. Chen, "A behavior mining based hybrid recommender system," In 2016 IEEE International Conference on Big Data Analysis (ICBDA), pp.1-5, March 2016. DOI: 10.1109/ICBDA.2016.7509785
  16. X. Xu, and D. Yuan, "A novel matrix factorization recommendation algorithm fusing social trust and behaviors in micro-blogs," 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), pp.283-287, April 2017, DOI: 10.1109/ICCCBDA.2017.7951925