Skip to main content
SpringerLink
Log in

Session Based Recommendations Using Char-Level Recurrent Neural Networks

  • Conference paper
  • First Online:
Advances in Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1463))

Included in the following conference series:

Abstract

The use of long short-term memory (LSTM) for session-based recommendations is described in this research. This study uses char-level LSTM as a real-time recommendation service to test and offer the optimal solution. Our strategy can be used to any situation. Two LSTM layers and a thick layer make up our model. To evaluate the prediction results, we use the mean of squared errors. We also put our recall and precision metrics prediction to the test. The best-performing network had roughly 2000 classes and was a trainer for the last year of likes on an image-based social platform. On twenty objects, our best model had a recall value of 0.182 and a precision value of 0.061.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdel-Nasser, M., Mahmoud, K.: Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput. Appl. 31(7), 2727–2740 (2017). https://doi.org/10.1007/s00521-017-3225-z

    Article  Google Scholar 

  2. Ciresan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649. IEEE, New York (2012). wOS:000309166203102

    Google Scholar 

  3. Dobrovolny, M., Mls, K., Krejcar, O., Mambou, S., Selamat, A.: Medical image data upscaling with generative adversarial networks. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds.) IWBBIO 2020. LNCS, vol. 12108, pp. 739–749. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45385-5_66

    Chapter  Google Scholar 

  4. Dobrovolny, M., Selamat, A., Krejcar, O.: Session based recommendations using recurrent neural networks - long short-term memory. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds.) ACIIDS 2021. LNCS (LNAI), vol. 12672, pp. 53–65. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73280-6_5

    Chapter  Google Scholar 

  5. Dobrovolny, M., Soukal, I., Lim, K.C., Selamat, A., Krejcar, O.: Forecasting of FOREX price trend using recurrent neural network - long short-term memory, pp. 95–103 April 2020. 10.36689/uhk/hed/2020-01-011, http://hdl.handle.net/20.500.12603/212

  6. Gunawardana, A., Shani, G.: Evaluating recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 265–308. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_8

    Chapter  Google Scholar 

  7. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. 5(4), 1–19 (2016). https://doi.org/10.1145/2827872

    Article  Google Scholar 

  8. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006). https://doi.org/10.1126/science.1127647, wOS:000239308600057

  9. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  10. Kuchaiev, O., Ginsburg, B.: Training deep autoencoders for collaborative filtering. arXiv:1708.01715 (2017)

  11. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), pp. 105–114. IEEE, New York (2017). wOS:000418371400012

    Google Scholar 

  12. Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., Alsaadi, F.E.: A survey of deep neural network architectures and their applications. Neurocomputing 234, 11–26 (2017). https://doi.org/10.1016/j.neucom.2016.12.038, wOS:000395221800002

  13. Mambou, S., Krejcar, O., Selamat, A., Dobrovolny, M., Maresova, P., Kuca, K.: Novel thermal image classification based on techniques derived from mathematical morphology: case of breast cancer. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds.) IWBBIO 2020. LNCS, vol. 12108, pp. 683–694. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45385-5_61

    Chapter  Google Scholar 

  14. Pena-Barragan, J.M., Ngugi, M.K., Plant, R.E., Six, J.: Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Envir. 115(6), 1301–1316 (2011). https://doi.org/10.1016/j.rse.2011.01.009

    Article  Google Scholar 

  15. Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: AutoRec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 111–112. Association for Computing Machinery, Florence, May 2015. https://doi.org/10.1145/2740908.2742726

  16. Sun, Y., Chen, J., Liu, Q., Liu, G.: Learning image compressed sensing with sub-pixel convolutional generative adversarial network. Pattern Recogn. 98, 107051 (2020). https://doi.org/10.1016/j.patcog.2019.107051, http://www.sciencedirect.com/science/article/pii/S003132031930353X

  17. Vaiyapuri, T., Binbusayyis, A.: Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation. PeerJ Comput. Sci. 6, e327 (2020). https://doi.org/10.7717/peerj-cs.327 wOS:000599181100001

  18. Varsamopoulos, S., Bertels, K., Almudever, C.G.: Designing neural network based decoders for surface codes, p. 13 (2018)

    Google Scholar 

  19. Wolterink, J.M., Leiner, T., Viergever, M.A., Isgum, I.: Generative adversarial networks for noise reduction in low-dose CT. IEEE Trans. Med. Imaging 36(12), 2536–2545 (2017). https://doi.org/10.1109/TMI.2017.2708987, wOS:000417913600013

  20. Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining WSDM 2016, pp. 153–162. Association for Computing Machinery, San Francisco, California February 2016. https://doi.org/10.1145/2835776.2835837

  21. Yang, Q., et al.: Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss. IEEE Trans. Med. Imaging 37(6), 1348–1357 (2018). https://doi.org/10.1109/TMI.2018.2827462, wOS:000434302700006

Download references

Acknowledgment

The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic.

Author information

Authors and Affiliations

  1. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Hradec Kralove, Czech Republic

    Michal Dobrovolny, Jaroslav Langer, Ali Selamat & Ondrej Krejcar

  2. Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia

    Ali Selamat

  3. School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Malaysia

    Ali Selamat

Authors
  1. Michal Dobrovolny
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaroslav Langer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ali Selamat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ondrej Krejcar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ondrej Krejcar .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  2. VU Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. University of the West of England, Bristol, UK

    Elias Pimenidis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dobrovolny, M., Langer, J., Selamat, A., Krejcar, O. (2021). Session Based Recommendations Using Char-Level Recurrent Neural Networks. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-88113-9_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88112-2

  • Online ISBN: 978-3-030-88113-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation