참고문헌
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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