Abstract
Although recommender systems have been significantly developed for providing customized services to users in various domains, they still have some limitations regarding the extraction of users’ conditional preferences from their past selections when they are in a dynamic context. We propose a framework to automatically extract and learn users’ conditional and qualitative preferences in a gamified system taking into consideration the players’ past behaviour, without asking any information from the players. To do that, we construct CP-nets modeling users preferences via a procedure that employs multiple Information Criterion score functions within an heuristic algorithm to learn a Bayesian network. The approach has been validated experimentally in the challenge recommendation domain in an urban mobility gamified system.
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
Similar content being viewed by others
Notes
- 1.
To use the above feature selection algorithms, we took the advantage of FSelector [18] library.
- 2.
References
Amatriain, X., Basilico, J.: Past, present, and future of recommender systems: an industry perspective. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys 2016, pp. 211–214. ACM, New York (2016)
Bigot, D., Mengin, J., Zanuttini, B.: Learning probabilistic CP-nets from observations of optimal items. In: STAIRS, pp. 81–90 (2014)
Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Int. Res. 21(1), 135–191 (2004)
Brun, Y., et al.: A design space for self-adaptive systems. In: de Lemos, R., Giese, H., Müller, H.A., Shaw, M. (eds.) Software Engineering for Self-Adaptive Systems II. LNCS, vol. 7475, pp. 33–50. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-35813-5_2
Carvalho, A.M.: Scoring functions for learning Bayesian networks. INESC-ID, Technical report (2009)
Chevaleyre, Y., Koriche, F., Lang, J., Mengin, J., Zanuttini, B.: Learning ordinal preferences on multiattribute domains: the case of CP-nets. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 273–296. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14125-6_13
Coelho, H., et al.: An efficient procedure for collective decision-making with CP-nets. In: ECAI 2010: 19th European Conference on Artificial Intelligence, vol. 215, p. 375 (2010)
Cornelio, C., Goldsmith, J., Mattei, N., Rossi, F., Venable, K.B.: Dynamic and probabilistic CP-nets. In: MPREF 2013 (2013)
Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)
Feo, T.A., Resende, M.G.: Greedy randomized adaptive search procedures. J. Global Optim. 6(2), 109–133 (1995)
Guerin, J.T., Allen, T.E., Goldsmith, J.: Learning CP-net preferences online from user queries. In: Perny, P., Pirlot, M., Tsoukiàs, A. (eds.) ADT 2013. LNCS (LNAI), vol. 8176, pp. 208–220. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41575-3_16
Jensen, F.V.: An Introduction to Bayesian Networks. UCL Press, London (1996)
Khoshkangini, R., Pini, M.S., Rossi, F.: A design of context-aware framework for conditional preferences of group of users. In: Lee, R. (ed.) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. SCI, vol. 653, pp. 97–112. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33810-1_8
Khoshkangini, R., Pini, M.S., Rossi, F.: A self-adaptive context-aware group recommender system. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 250–265. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49130-1_19
Khoshkangini, R., Valetto, G., Marconi, A.: Generating personalized challenges to enhance the persuasive power of gamification. In: Personalization in Persuasive Technology Workshop (2017)
Khoshkangini, R., Valetto, G., Marconi, A.: Automatic generation and recommendation of personalized challenges for gamification. In: User Modeling and User-Adapted Interaction (UMUAI) (2019, under review)
Koriche, F., Zanuttini, B.: Learning conditional preference networks. Artif. Intell. 174(11), 685–703 (2010)
Kursa, M.B., Rudnicki, W.R., et al.: Feature selection with the boruta package. J. Stat. Softw. 36(11), 1–13 (2010)
Lee, C., Lee, G.G.: Information gain and divergence-based feature selection for machine learning-based text categorization. Inf. Process. Manag. 42(1), 155–165 (2006)
Lessel, P., Altmeyer, M., Krüger, A.: Analysis of recycling capabilities of individuals and crowds to encourage and educate people to separate their garbage playfully. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015 (2015)
Liu, Z., Malone, B., Yuan, C.: Empirical evaluation of scoring functions for Bayesian network model selection. BMC Bioinform. 13(15), S14 (2012)
Lopes, R., Bidarra, R.: Adaptivity challenges in games and simulations: a survey. IEEE Trans. Comput. Intell. AI Games 3(2), 85–99 (2011)
Marchetti-Spaccamela, A., Vitaletti, A., Becchetti, L., Colesanti, U.: Self-adaptive recommendation systems: models and experimental analysis. In: Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems (2008)
Neal, R.M.: Bayesian Learning for Neural Networks, vol. 118. Springer, New York (2012). https://doi.org/10.1007/978-1-4612-0745-0
Ono, C., Kurokawa, M., Motomura, Y., Asoh, H.: A context-aware movie preference model using a Bayesian network for recommendation and promotion. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 247–257. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73078-1_28
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach Author (2009)
Yamaoka, K., Nakagawa, T., Uno, T.: Application of Akaike’s information criterion (AIC) in the evaluation of linear pharmacokinetic equations. J. Pharmacokinet. Biopharm. 6(2), 165–175 (1978)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Khoshkangini, R., Pini, M.S., Rossi, F. (2019). Constructing CP-Nets from Users Past Selection. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-35288-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35287-5
Online ISBN: 978-3-030-35288-2
eBook Packages: Computer ScienceComputer Science (R0)