Skip to main content
SpringerLink
Log in

Socially Assistive Robot for Providing Recommendations: Comparing a Humanoid Robot with a Mobile Application

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

A Correction to this article was published on 06 August 2018

This article has been updated

Abstract

Socially assistive robotics has gained a valuable role in assisting, influencing and motivating the human behavior in many Human–Machine Interaction contexts. This finding suggested the use of social robots as recommendation interfaces. Despite many other types of recommending technologies exist (e.g., virtual agents, apps on cell phones, etc.), experimental studies convey that human beings result to be more engaged and influenced by the interaction with robots with respect to these other technologies. In particular, this has been shown by comparing social robots with virtual agents; a poor literature copes with the comparison between social robots and applications on mobile phones. To this extent, in this work, we address the comparison between these latest two technologies in the context of movie recommendation, where the two considered interfaces are programmed to provide the same contents, but through different communication channels. We provide the results of two experimental studies with the aim of evaluating the quality of the interaction from both the point of view of the application (by considering the users’ acceptance rate of the recommendations) and of the users (by analyzing the users’ evaluations) while interacting with the two interfaces. The main result arising from this study is that the social robot is preferred by users although, apparently, it does not change the acceptance rate of the proposed movies.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

  • 06 August 2018

    In the original publication, authors Silvia Rossi and Anna Tamburro’s affiliations have been incorrectly reversed and should to be read correctly as “Department of Electrical Engineering and Information Technology”.

  • 06 August 2018

    In the original publication, authors Silvia Rossi and Anna Tamburro���s affiliations have been incorrectly reversed and should to be read correctly as ���Department of Electrical Engineering and Information Technology���.

  • 06 August 2018

    In the original publication, authors Silvia Rossi and Anna Tamburro���s affiliations have been incorrectly reversed and should to be read correctly as ���Department of Electrical Engineering and Information Technology���.

  • 06 August 2018

    In the original publication, authors Silvia Rossi and Anna Tamburro���s affiliations have been incorrectly reversed and should to be read correctly as ���Department of Electrical Engineering and Information Technology���.

  • 06 August 2018

    In the original publication, authors Silvia Rossi and Anna Tamburro���s affiliations have been incorrectly reversed and should to be read correctly as ���Department of Electrical Engineering and Information Technology���.

  • 06 August 2018

    In the original publication, authors Silvia Rossi and Anna Tamburro���s affiliations have been incorrectly reversed and should to be read correctly as ���Department of Electrical Engineering and Information Technology���.

Notes

  1. http://www.omdbapi.com—The Open Movie Database is a free web service to obtain movie information.

References

  1. Alemi M, Meghdari A, Ghazisaedy M (2015) The impact of social robotics on l2 learners’ anxiety and attitude in english vocabulary acquisition. Int J Soc Robot 7(4):523–535

    Article  Google Scholar 

  2. Bader N, Mokryn O, Lanir J (2017) Exploring emotions in online movie reviews for online browsing. In: Proceedings of the 22nd international conference on intelligent user interfaces companion, pp 35–38. ACM

  3. Bainbridge W, Hart J, Kim E, Scassellati B (2008) The effect of presence on human–robot interaction. In: The 17th IEEE international symposium on robot and human interactive communication (RO-MAN), pp. 701–706

  4. Bartneck C, Bleeker T, Bun J, Fens P, Riet L (2010) The influence of robot anthropomorphism on the feelings of embarrassment when interacting with robots. Paladyn J Behav Robot 1(2):109–115

    Google Scholar 

  5. Boone RT, Buck R (2003) Emotional expressivity and trustworthiness: the role of nonverbal behavior in the evolution of cooperation. J Nonverbal Behav

  6. Cavallo F, Aquilano M, Carrozza M, Dario P (2012) Robot-era project: the vision of 3d service robotics. Gerontechnology 11(2)

  7. Cervone F, Sica V, Staffa M, Tamburro A, Rossi S (2015) Comparing a social robot and a mobile application for movie recommendation: a pilot study. In: Proceedings of the 16th workshop from objects to agents, CEUR workshop proceedings, pp 32–38

  8. Dautenhahn K, Werry I (2004) Towards interactive robots in autism therapy: background, motivation and challenges. Pragmat Cognit 12(1):1–35

    Article  Google Scholar 

  9. Feil-Seifer D, Mataric MJ (2005) Defining socially assistive robotics. In: 9th International conference on rehabilitation robotics, pp 465–468

  10. Ham J, Bokhorst R, Cuijpers R, van der Pol D, Cabibihan JJ (2011) Making robots persuasive: the influence of combining persuasive strategies (gazing and gestures) by a storytelling robot on its persuasive power. In: Proceedings of the third international conference on social robotics, ICSR’11. Springer, Berlin, pp 71–83

  11. Iengo S, Rossi S, Staffa M, Finzi A (2014) Continuous gesture recognition for flexible human–robot interaction. In: ICRA. IEEE, pp 4863–4868

  12. John OP, Naumann LP, Soto CJ (2008) Paradigm shift to the integrative big five trait taxonomy: history, measurement, and conceptual issues. In: John OP, Robbins RW, Pervin LA (eds) Handbook of personality: theory and research. Guilford, New York, pp 114–156

    Google Scholar 

  13. Johnson DO, Cuijpers RH, Juola JF, Torta E, Simonov M, Frisiello A, Bazzani M, Yan W, Weber C, Wermter S, Meins N, Oberzaucher J, Panek P, Edelmayer G, Mayer P, Beck C (2014) Socially assistive robots: a comprehensive approach to extending independent living. Int J Social Robot 6(2):195–211

    Article  Google Scholar 

  14. Kidd C, Breazeal C (2004) Effect of a robot on user perceptions. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), vol 4, pp 3559–3564

  15. Konstan JA, Riedl J (2014) Recommender systems: from algorithms to user experience. User Model User-Adapt Interact 22(1):101–123

    Google Scholar 

  16. Lim A, Ogata T, Okuno HG (2012) The desire model: Cross-modal emotion analysis and expression for robots. 74(5):4

  17. Matarić MJ (2017) Socially assistive robotics: human augmentation versus automation. Sci Robot 2(4)

  18. McCrae RR, Costa PT (1987) Validation of the five-factor model of personality across instruments and observers. J Pers Soc Psychol 52(1):81

    Article  Google Scholar 

  19. de Melo CM, Zheng L, Gratch J (2009) Expression of moral emotions in cooperating agents. In: Proceedings of the 9th international conference on intelligent virtual agents (IVA)

  20. Merritt SM, Ilgen DR (2008) Not all trust is created equal: dispositional and history-based trust in human–automation interactions. Hum Factors 50(2):194–210

    Article  Google Scholar 

  21. Murphy-Hill E, Murphy G (2014) Recommendation delivery. In: Robillard MP, Maalej W, Walker RJ, Zimmermann T (eds) Recommendation systems in software engineering. Springer, Berlin, pp 223–242

    Chapter  Google Scholar 

  22. Posner J, Russell JA, Peterson BS (2005) The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev Psychopathol 17(03):715–734

    Article  Google Scholar 

  23. Powers A, Kiesler S, Fussell S, Torrey C (2007) Comparing a computer agent with a humanoid robot. In: 2nd ACM/IEEE international conference on human–robot interaction (HRI), pp 145–152

  24. Qiu L, Benbasat I (2009) Evaluating anthropomorphic product recommendation agents: a social relationship perspective to designing information systems. J Manag Inf Syst 25(4):145–182

    Article  Google Scholar 

  25. Rossi S, Cervone F (2016) Social utilities and personality traits for group recommendation: a pilot user study. In: Proceedings of the 8th international conference on agents and artificial intelligence, pp 38–46

  26. Rossi S, Ferland F, Tapus A (2017) User profiling and behavioral adaptation for HRI: a survey. Pattern Recognit Lett 99(Supplement C):3–12

    Article  Google Scholar 

  27. Rossi S, Staffa M, Giordano M, De Gregorio M, Rossi A, Tamburro A, Vellucci C (2015) Robot head movements and human effort in the evaluation of tracking performance. In: 24th IEEE international symposium on robot and human interactive communication (RO-MAN), pp 791–796

  28. Salichs MA, Encinar IP, Salichs E, Castro-González Á, Malfaz M (2016) Study of scenarios and technical requirements of a social assistive robot for alzheimers disease patients and their caregivers. Int J Soc Robot 8(1):85–102

    Article  Google Scholar 

  29. Shinozawa K, Naya F, Yamato J, Kogure K (2005) Differences in effect of robot and screen agent recommendations on human decision-making. Int J Hum Comput Stud 62(2):267–279

    Article  Google Scholar 

  30. Shiomi M, Shinozawa K, Nakagawa Y, Miyashita T, Sakamoto T, Terakubo T, Ishiguro H, Hagita N (2013) Recommendation effects of a social robot for advertisement-use context in a shopping mall. Int J Soc Robot 5(2):251–262

    Article  Google Scholar 

  31. Song S, Yamada S (2017) Expressing emotions through color, sound, and vibration with an appearance-constrained social robot. In: Proceedings of the 2017 ACM/IEEE international conference on human–robot interaction, pp 2–11. ACM

  32. Staffa M, Gregorio MD, Giordano M, Rossi S (2014) Can you follow that guy? In: 22th European symposium on artificial neural networks ESANN, Bruges, Belgium, April 23–25, pp 511–516

  33. Staffa M, Rossi S (2016) Recommender interfaces: the more human-like, the more humans like. In: Social robotics: 8th international conference. ICSR 2016, Kansas City, MO, USA, November 1–3, proceedings. Springer, Cham, pp 200–210

  34. Tapus A, Tăpus C, Matarić MJ (2008) User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intell Serv Robot 1(2):169–183

    Article  Google Scholar 

  35. Tkalcic M, Chen L (2015) Personality and recommender systems. Springer, Berlin, pp 715–739

    Google Scholar 

  36. Wang YD, Emurian HH (2005) An overview of online trust: concepts, elements, and implications. Comput Hum Behav 21(1):105–125

    Article  Google Scholar 

  37. Xu J, Broekens J, Hindriks K, Neerincx MA (2013) Mood expression through parameterized functional behavior of robots. In: 2013 IEEE RO-MAN, pp 533–540

  38. Yoo KH, Gretzel U (2011) Creating more credible and persuasive recommender systems: The influence of source characteristics on recommender system evaluations. In: Recommender systems handbook. Springer, US, pp 455–477

Download references

Author information

Authors and Affiliations

  1. Department of Physics, University of Naples Federico II, Naples, Italy

    Silvia Rossi & Anna Tamburro

  2. Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy

    Mariacarla Staffa

Authors
  1. Silvia Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariacarla Staffa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anna Tamburro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Silvia Rossi.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Funding

This study was has been partially supported by MIUR (Italian Ministry of Education, Universities, and Research) within the PRIN 2015 research project “UPA4SAR - User-centered Profiling and Adaptation for Socially Assistive Robotics” (Grant no. 2015KB-L78T).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rossi, S., Staffa, M. & Tamburro, A. Socially Assistive Robot for Providing Recommendations: Comparing a Humanoid Robot with a Mobile Application. Int J of Soc Robotics 10, 265–278 (2018). https://doi.org/10.1007/s12369-018-0469-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-018-0469-4

Keywords

Search

Navigation