Constructing CP-Nets from Users Past Selection

  • Reza KhoshkanginiEmail author
  • Maria Silvia Pini
  • Francesca Rossi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11919)


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.


CP-net Bayesian network Recommender system Gamification 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Reza Khoshkangini
    • 1
    Email author
  • Maria Silvia Pini
    • 2
  • Francesca Rossi
    • 3
  1. 1.Center for Applied Intelligent Systems Research (CAISR)Halmstad UniversityHalmstadSweden
  2. 2.Department of Information EngineeringUniversity of PadovaPaduaItaly
  3. 3.IBM T. J. Watson Research CenterYorktown HeightsUSA

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