Skip to main content

Abstract

The number of people who attend virtual meetings has increased as a result of COVID-19. In this paper, we present a system that consists of an expressive humanoid social robot called QTRobot, and a recommender system that employs natural language processing techniques to recommend images related to the content of the presenter’s speech to the audience in real time. This is achieved utilising the QTRobot’s platform capabilities (microphone, computation power, and Wi-Fi).

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Similar content being viewed by others

Notes

  1. 1.

    https://commons.wikimedia.org.

References

  1. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  2. DeFilippis, E., Impink, S.M., Singell, M., Polzer, J.T., Sadun, R.: Collaborating during coronavirus: the impact of Covid-19 on the nature of work. Technical report, National Bureau of Economic Research (2020)

    Google Scholar 

  3. Ehnes, J.: A tangible interface for the AMI content linking device-the automated meeting assistant. In: 2009 2nd Conference on Human System Interactions, pp. 306–313. IEEE (2009)

    Google Scholar 

  4. Stroessner, S.J., Benitez, J.: The social perception of humanoid and non-humanoid robots: effects of gendered and machinelike features. Int. J. Soc. Robot. 11(2), 305–315 (2019)

    Article  Google Scholar 

Download references

Acknowledgments

This work has been funded by the Luxembourg National Research Fund under Grant No. IS/14717072.

This work is partially supported by the Chist-Era grant CHIST-ERA19-XAI-005, and by (i) the Swiss National Science Foundation (G.A. 20CH21_195530), (ii) the Italian Ministry for Universities and Research, (iii) the Luxembourg National Research Fund (G.A. INTER/CHIST/19/14589586), (iv) the Scientific and Research Council of Turkey (TÜBİTAK, G.A. 120N680).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benoît Alcaraz .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alcaraz, B., Hosseini-Kivanani, N., Najjar, A. (2022). IRRMA: An Image Recommender Robot Meeting Assistant. In: Dignum, F., Mathieu, P., Corchado, J.M., De La Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Complex Systems Simulation. The PAAMS Collection. PAAMS 2022. Lecture Notes in Computer Science(), vol 13616. Springer, Cham. https://doi.org/10.1007/978-3-031-18192-4_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-18192-4_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18191-7

  • Online ISBN: 978-3-031-18192-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics