Preference Modeling for Adaptive Storytelling

In almost all forms of storytelling, the background and the current state of mind of the audience members predispose them to experience a given story from a uniquely personal perspective. However, traditional story writers usually construct their narratives based on the average preferences of their audience, which does not guarantee satisfying narrative experiences for its members. When a narrative aims at providing pleasurable entertainment, having some information about the preferences of the current user for the narrative’s content is vital to create satisfying experiences. This project explores user modeling and adaptive storytelling to generate individualized interactive narratives based on the preferences of users.

Status:

Contributions:
  • We propose a novel approach to generate individualized interactive narratives based on the preferences of users, which are modeled in terms of the Big Five factors.
  • The proposed method allows narrative to be automatically adapted according to the user’s personality traits.
  • Results show that the proposed method improves user satisfaction and experience.
Publications:
  • Edirlei Soares de Lima; Bruno Feijó; Antonio L. Furtado. Adaptive Storytelling Based on Personality and Preference Modeling. Entertainment Computing, Volume 34, May 2020, 100342, 2020. [DOI]

  • Best Paper Award Edirlei Soares de Lima; Bruno Feijó; Antonio L. Furtado; Vinícius Michel Gottin. Personality and Preference Modeling for Adaptive Storytelling. Proceedings of the XVII Brazilian Symposium on Computer Games and Digital Entertainment (SBGames 2018), Foz do Iguaçu, Brazil, p. 538-547, 2018. [Best Paper Award] [PDF] [DOI]

Media: