Skip to main content
Springer Nature Link
Log in

Validation of 3D Convolutional Neural Networks for Customer Satisfaction Estimation Using Videos

  • Conference paper
  • First Online:
Advances in Network-Based Information Systems (NBiS 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 7))

Included in the following conference series:

Abstract

Companies and public entities administer customer satisfaction surveys to identify business problems. However, devising and analyzing questionnaires is burdensome for investigators, and answering questionnaires imposes a burden on customers. In addition, the response rate is frequently low. Here, to address these problems, we attempt to estimate customer satisfaction using sensing technology. We hypothesize that satisfaction can be discerned through facial expressions and body movements. To validate this hypothesis, we applied three-dimensional convolution neural networks.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Chang, T.Z., Wildt, A.R.: Impact of product information on the use of price as a quality cue. Psychol. Mark. 13(1), 55–75 (1996)

    Article  Google Scholar 

  2. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (2011)

    MathSciNet  MATH  Google Scholar 

  3. Howard, J.A., Hulbert, J.: Advertising and the public interest (1973)

    Google Scholar 

  4. Jacoby, J., Olson, J.C., Haddock, R.A.: Price, brand name, and product composition characteristics as determinants of perceived quality. J. Appl. Psychol. 55(6), 570 (1971)

    Article  Google Scholar 

  5. Ji, S., Xu, W., Yang, M., Yu, K.: 3d convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 221–231 (2013)

    Article  Google Scholar 

  6. Kingma, D., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv preprint: arXiv:1412.6980

  7. Matsukawa, T., Hidaka, A., Kurita, T.: Classification of spectators’ state in video sequences by voting of facial expressions and face directions. In: MVA, pp. 426–430 (2009)

    Google Scholar 

  8. Shimaguchi, M.: Theoretical hypothesis of consumer satisfaction structure and marketing strategy behavior. Keio Bus. Forum 2(3), 43–69 (1981). (in Japanese)

    Google Scholar 

  9. Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  10. Uehara, S.: A study for the influence of service quality to customer satisfaction. Kaetsu Univ. Res. Study 52(1), 1–15 (2009). (in Japanese)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan

    Tomofumi Nakano & Shohei Kato

  2. Frontier Research Institute for Information Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan

    Shohei Kato

Authors
  1. Tomofumi Nakano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shohei Kato
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomofumi Nakano .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Rissho University, Tokyo, Japan

    Tomoya Enokido

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Nakano, T., Kato, S. (2018). Validation of 3D Convolutional Neural Networks for Customer Satisfaction Estimation Using Videos. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65521-5_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65520-8

  • Online ISBN: 978-3-319-65521-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics