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Fits Like a Game: A Multi-criteria Adaptive Gamification for Collaborative Location-Based Collecting Systems

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HCI in Games (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14046))

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Abstract

This article proposes an adaptive gamification approach based on a Multi-Criteria Recommendation System (MCRS) for Collaborative Location-based Collecting Systems, adapting the gamification to each user, taking into account her preferences and the project’s objectives as a multi-criteria scenario. Specifically, the potentially recommended items are dynamically generated gamification elements, and the recommendation criteria are defined considering two points of view: user preferences and project objectives. Finally, the article includes an evaluation of the proposal and then a discussion of the results.

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Authors and Affiliations

  1. Depto CyT, Universidad Nacional de Quilmes, R. Saenz Peña 352, Bernal, Buenos Aires, Argentina

    María Dalponte Ayastuy & Diego Torres

  2. LIFIA, CICPBA-Facultad de Informática, Universidad Nacional de La Plata, 50 y 120, La Plata, Buenos Aires, Argentina

    María Dalponte Ayastuy, Alejandro Fernández & Diego Torres

Authors
  1. María Dalponte Ayastuy
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  2. Alejandro Fernández
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  3. Diego Torres
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Corresponding author

Correspondence to María Dalponte Ayastuy .

Editor information

Editors and Affiliations

  1. DePaul University, Chicago, IL, USA

    Xiaowen Fang

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Dalponte Ayastuy, M., Fernández, A., Torres, D. (2023). Fits Like a Game: A Multi-criteria Adaptive Gamification for Collaborative Location-Based Collecting Systems. In: Fang, X. (eds) HCI in Games. HCII 2023. Lecture Notes in Computer Science, vol 14046. Springer, Cham. https://doi.org/10.1007/978-3-031-35930-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-35930-9_19

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