Abstract
In this work, a convolutional neural network is used to predict the interest of social networks users in certain product categories. The goal is to make a multi-class image classification to target social networks users as potential products consumers. In this paper, we compare the performance of several artificial neural network training algorithms using adaptive learning: stochastic gradient descent, adaptive gradient descent, adaptive moment estimation and its version based on infinity norm and root mean square prop. The comparison of the training algorithms shows that the algorithm based on adaptive moment estimation is the most appropriate to predict user’s interest and profile, achieving about 99% classification accuracy .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ajeet Ram Pathak, M.P., Rautaray, S.: Application of deep learning for object detection. Procedia Comput. Sci. 132, 1706–1717 (2018). https://doi.org/10.1016/j.procs.2018.05.144, http://www.sciencedirect.com/science/article/pii/S1877050918308767. International Conference on Computational Intelligence and Data Science
Aslam, S.: Omnicoreagency: Instagram statistics & facts for 2019 (2019). https://www.websitehostingrating.com/instagram-statistics/. Instagram by the numbers: usage stats, demographics and facts you need to know
Cinar, Y.G., Zoghbi, S., Moens, M.: Inferring user interests on social media from text and images. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp. 1342–1347, November 2015. https://doi.org/10.1109/ICDMW.2015.208
Image classification: car, motorbike, bicycle (2012). https://www.kaggle.com/c/image-classification2/data
Competition distinguish dogs from cats (2014). https://www.kaggle.com/c/dogs-vs-cats/overview
Keyes, D.: Instagram rolls out shoppable posts for more merchants (2017). https://www.businessinsider.com/instagram-rolls-out-shoppable-posts-for-more-merchants-2017-10
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Li, F.F., Andreetto, M., Ranzato, M.A.: Caltech101 image dataset (2003). http://www.vision.caltech.edu/Image_Datasets/Caltech101/
Maksimov, A.: Docker container with jupyter, matplotlib, pandas, tensorflow, keras and opencv (2019). https://hub.docker.com/r/amaksimov/python_data_science/
Rosebrock, A.: Multi-label classification with keras (2018). https://www.pyimagesearch.com/2018/05/07/
SciKit-learn.org: Machine learn in pyton (2019). https://scikit-learn.org/stable/
Segalin, C., Perina, A., Cristani, M., Vinciarelli, A.: The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits. IEEE Trans. Affect. Comput. 8(2), 268–285 (2017). https://doi.org/10.1109/TAFFC.2016.2516994
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
You, Q., Bhatia, S., Sun, T., Luo, J.: The eyes of the beholder: gender prediction using images posted in online social networks. In: 2014 IEEE International Conference on Data Mining Workshop, pp. 1026–1030, December 2014. https://doi.org/10.1109/ICDMW.2014.93
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
de Azevedo, V.R., Nedjah, N., de Macedo Mourelle, L. (2020). Identification of Client Profile Using Convolutional Neural Networks. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-58808-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58807-6
Online ISBN: 978-3-030-58808-3
eBook Packages: Computer ScienceComputer Science (R0)