Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware?

Fanjuan Shi, Jean-Luc Marini

Abstract

This paper presents the result of a controlled experiment studying how mood state can affect the usage of e-commerce recommender system. The authors develop a mood recognition tool to classify online shoppers into stressed or relaxed mood state unobtrusively. By analyzing their reactions to recommended products when surfing on an e-commerce website, the authors make two conclusions. Firstly, stress negatively impacts the usage of recommender system. Secondly, relaxed users are more receptive to recommendations. These findings suggest that mood recognition tool can help recommender systems find the "right time" to intervene. And mood-aware recommender systems can enhance marketer-consumer interaction.

References

  1. Fridlund, A. J., 2014. Human facial expression: An evolutionary view. Academic Press.
  2. Koolagudi, S. G., & Rao, K. S., 2012. Emotion recognition from speech: a review. International journal of speech technology, 15(2), 99-117.
  3. Silva, D. C., Vinhas, V., Reis, L. P., & Oliveira, E., 2009. Biometric emotion assessment and feedback in an immersive digital environment. International Journal of Social Robotics, 1(4), 307-317.
  4. Baldoni, M., Baroglio, C., Patti, V., & Rena, P., 2012. From tags to emotions: Ontology-driven sentiment analysis in the social semantic web. Intelligenza Artificiale, 6(1), 41-54.
  5. Khan, I. A., Brinkman, W. P., & Hierons, R., 2013. Towards estimating computer users' mood from interaction behaviour with keyboard and mouse. Frontiers of Computer Science, 7(6), 943-954.
  6. Sebe, N., Cohen, I., Gevers, T., & Huang, T. S., 2006. Emotion recognition based on joint visual and audio cues. In 18th International Conference on Pattern Recognition, Vol. 1, 1136-1139. IEEE.
  7. Ambinder, M., 2011. Biofeedback in gameplay: How valve measures physiology to enhance gaming experience. In Game Developers Conference. Vol. 2011.
  8. D'Mello, S., Jackson, T., Craig, S., Morgan, B., Chipman, P., White, H., & Graesser, A., 2008. AutoTutor detects and responds to learners affective and cognitive states. In Workshop on Emotional and Cognitive Issues at the International Conference on Intelligent Tutoring Systems, 306-308.
  9. Mao, X., & Li, Z., 2009. Implementing emotion-based user-aware e-learning. In CHI'09 Extended Abstracts on Human Factors in Computing Systems, 3787-3792. ACM.
  10. Bailey, B. P., & Konstan, J. A., 2006. On the need for attention-aware systems: Measuring effects of interruption on task performance, error rate, and affective state. Computers in human behavior, 22(4), 685-708.
  11. Kolakowska, A., Landowska, A., Szwoch, M., Szwoch, W., & Wrobel, M. R, 2013. Emotion recognition and its application in software engineering. In The 6th International Conference on Human System Interaction, 532-539. IEEE.
  12. Lane, A. M., & Terry, P. C., 2000. The nature of mood: Development of a conceptual model with a focus on depression. Journal of Applied Sport Psychology, 12(1), 16-33.
  13. Lee, H., Choi, Y. S., Lee, S., & Park, I. P., 2012. Towards unobtrusive emotion recognition for affective social communication. In Consumer Communications and Networking Conference (CCNC), 260-264. IEEE.
  14. Epp, C., Lippold, M., & Mandryk, R. L., 2011. Identifying emotional states using keystroke dynamics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 715-724. ACM.
  15. Zimmermann, P., Gomez, P., Danuser, B., & Schär, S. (2006). Extending usability: putting affect into the user-experience. Proceedings of NordiCHI'06, 27-32.
  16. Khanna, P., & Sasikumar, M. (2010). Recognising emotions from keyboard stroke pattern. International journal of computer applications, 11(9), 1-5.
  17. Lv, H. R., Lin, Z. L., Yin, W. J., & Dong, J., 2008. Emotion recognition based on pressure sensor keyboards. In IEEE International Conference on Multimedia and Expo, 1089-1092. IEEE.
  18. Sottilare, R. A., & Proctor, M., 2012. Passively classifying student mood and performance within intelligent tutors. Journal of Educational Technology & Society, 15(2), 101-114.
  19. Maehr, W., 2005. Estimation of the User's Emotional State by Mouse Motions. Doctoral dissertation for Fachhochschule Vorarlberg, Dornbirn, Austria.
  20. Vizer, L. M., Zhou, L., & Sears, A., 2009. Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886.
  21. Zimmermann, P., Guttormsen, S., Danuser, B., & Gomez, P., 2003. Affective computing - a rationale for measuring mood with mouse and keyboard. International journal of occupational safety and ergonomics, 9(4), 539-551.
  22. Salmeron-Majadas, S., Santos, O. C., & Boticario, J. G., 2014. Exploring indicators from keyboard and mouse interactions to predict the user affective state. In Educational Data Mining 2014.
  23. Lee, P. M., Tsui, W. H., & Hsiao, T. C., 2012. A low-cost scalable solution for monitoring affective state of students in e-learning environment using mouse and keystroke data. In Intelligent Tutoring Systems. 679- 680. Springer Berlin Heidelberg.
  24. Salmeron-Majadas, S., Santos, O. C., Boticario, J. G., Cabestrero, R., Quirós, P., & Saneiro, M., 2013. Gathering emotional data from multiple sources. In Educational Data Mining.
  25. Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. C., 2004. Modelling online browsing and path analysis using clickstream data. Marketing Science, 23(4), 579- 595.
  26. Bucklin, R. E., & Sismeiro, C., 2009. Click here for Internet insight: Advances in clickstream data analysis in marketing. Journal of Interactive Marketing, 23(1), 35-48.
  27. Chen, L., & Su, Q., 2013. Discovering user's interest at Ecommerce site using clickstream data. In Service systems and service management (ICSSSM), 2013 10th international conference on. 124-129. IEEE.
  28. Olbrich, R., & Holsing, C., 2011. Modeling consumer purchasing behavior in social shopping communities with clickstream data. International Journal of Electronic Commerce, 16(2), 15-40.
  29. Arnold, M. J., & Reynolds, K. E., 2003. Hedonic shopping motivations. Journal of retailing, 79(2), 77-95.
  30. Horvitz, E., Kadie, C., Paek, T., & Hovel, D., 2003. Models of attention in computing and communication: from principles to applications. Communications of the ACM, 46(3), 52-59.
  31. Fischer, G., 2012. Context-aware systems: the “right” information, at the “right” time, in the “right” place, in the “right” way, to the “right” person. In Proceedings of the International Working Conference on Advanced Visual Interfaces. 287-294. ACM.
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Paper Citation


in Harvard Style

Shi F. and Marini J. (2016). Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware? . In Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST, ISBN 978-989-758-186-1, pages 173-180. DOI: 10.5220/0005618901730180


in Bibtex Style

@conference{webist16,
author={Fanjuan Shi and Jean-Luc Marini},
title={Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware?},
booktitle={Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,},
year={2016},
pages={173-180},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005618901730180},
isbn={978-989-758-186-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Web Information Systems and Technologies - Volume 2: WEBIST,
TI - Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware?
SN - 978-989-758-186-1
AU - Shi F.
AU - Marini J.
PY - 2016
SP - 173
EP - 180
DO - 10.5220/0005618901730180