Abstract
This paper proposes a novel method for user profiling in recommender systems (RS). RS have emerged as a key tool in information filtering. But despite their importance in our lives, systems still suffer from the cold-start problem: the inability to infer preferences of a new user who has not rated enough items. Up till now, only limited research has focused on optimizing user profile acquisition processes. This paper addresses that gap, employing a gamified personality-acquisition system based on the widely used Five Factor Model (FFM) for assessing personality. Our web-based system accurately extrapolates a user’s preferences by guiding them through a series of interactive and contextualized questions. This paper demonstrates the efficacy of a gamified user profiling system that employs story-based questions derived from explicit personality inventory questions. The Gamified Personality Acquisition (GPA) system was shown to increase Mean Absolute Error (MAE) and Receiver Operating Characteristic (ROC) sensitivity in a travel RS while mitigating the cold-start problem in comparison to rating-based and traditional personality-based RS.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Cambridge English: www.cambridgeenglish.org.
- 2.
Unity3d: http://unity3d.com.
- 3.
LIWC: http://liwc.net.
- 4.
References
Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. pp. 1–40
Berinsky, A.J., et al.: Evaluating online labor markets for experimental research: Amazon.com’s Mechanical Turk. Polit. Anal. 20, 351–368 (2012)
Buhrmester, M., et al.: Amazon’s Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data? (2011)
Wright, W.R., Chin, D.N.: Personality profiling from text: introducing part-of-speech N-grams. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, G.-J. (eds.) UMAP 2014. LNCS, vol. 8538, pp. 243–253. Springer, Heidelberg (2014)
Costa Jr., P.T., McCrae, R.R.: Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory professional manual. Psychological Assessment Resources, Odessa (1992)
DeMars, C.: Item Response Theory. Oxford University Press, Oxford (2010)
Dennis, M., et al.: The quest for validated personality trait stories. In: Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces – IUI 2012, p. 273. ACM Press, New York (2012)
Dey, A.K.: Understanding and Using Context (2001)
Dunn, G., Wiersema, J., Ham, J., Aroyo, L.: Evaluating interface variants on personality acquisition for recommender systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 259–270. Springer, Heidelberg (2009)
Gosling, S.D., et al.: A very brief measure of the Big-Five personality domains. J. Res. Pers. 37(6), 504–528 (2003)
Hu, R., Pu, P.: Acceptance issues of personality-based recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems – RecSys 2009, p. 221. ACM Press, New York (2009)
Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the Fifth ACM Conference on Recommender Systems – RecSys 2011, p. 197. ACM Press, New York (2011)
Hu, R., Pu, P.: A comparative user study on rating vs. personality quiz based preference elicitation methods. In: International Conference on Intelligent User Interfaces (IUI) (2009)
Kittur, A., et al.: Crowdsourcing user studies with Mechanical Turk. In: Proceeding of the Twenty-Sixth Annual SIGCHI Conference on Human Factors in Computing Systems, pp. 453–456 (2008)
Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Model. User-adapt. Interact. 22(1–2), 101–123 (2012)
Mairesse, F.: Personality Recognizer Software. http://people.csail.mit.edu/francois/research/personality/demo.html
Acknowledgments
We are grateful to RB, a doctoral student of quantitative psychology, for his assistance during the IRT analysis. This work was supported by the National High-tech R&D Program of China (Grant No. SS2015AA020102), National Basic Research Program of China (Grant No. 2011CB302302), Tsinghua University Initiative Scientific Research Program.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Teklemicael, F., Zhang, Y., Wu, Y., Yin, Y., Xing, C. (2016). Toward Gamified Personality Acquisition in Travel Recommender Systems. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-31854-7_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31853-0
Online ISBN: 978-3-319-31854-7
eBook Packages: Computer ScienceComputer Science (R0)