Personalized story-viewing experiences on the Polariscope platform

  • Jónatas Roque University of Aveiro
  • Pedro Almeida University of Aveiro
  • Telmo Silva University of Aveiro
  • Ana Velhinho University of Aveiro
  • Mariana Alves University of Aveiro

Abstract


Digital storytelling is increasingly used to enrich digital content, and personalization features may add significant value to the story-viewing experiences. This research focuses on enhancing digital storytelling using personalized filtering and recommendation methods to support the development of a media visualization mode integrated into the Polariscope platform, aimed at bringing people together by sharing and co-creating collective memories. These personalization features intend to enable more dynamic and immersive story-viewing experiences based on user preferences and customized parameters, such as time-based, context-based, media types, among others. A prototype of this solution was presented to a focus group of six participants to gather feedback and suggestions on the proposed solutions. The results highlighted a strong appeal for these features, with a preference for the summary mode visualization and for the filtering based on spare time and media type. Control was one of the most valued aspects among participants, who favored manual personalization over automatic personalization. These insights, along with additional suggestions such as filtering content by relevance, by geographic region, and incorporating voiceovers, will be integrated into the next iteration of the prototype, which will undergo field testing with end-users.

Keywords: Digital storytelling, Filtering, Personalization modes, Recommender systems

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Published
2025-06-03
ROQUE, Jónatas; ALMEIDA, Pedro; SILVA, Telmo; VELHINHO, Ana; ALVES, Mariana. Personalized story-viewing experiences on the Polariscope platform. In: ACM INTERNATIONAL CONFERENCE ON INTERACTIVE MEDIA EXPERIENCES WORKSHOPS (IMXW), 25. , 2025, Niterói/RJ. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 37-41. DOI: https://doi.org/10.5753/imxw.2025.3888.