| G. Drakopoulos, Y. Voutos, Ph. Mylonas |
| Annotation-Assisted Clustering Of Player Profiles In Cultural Games: A Case For Tensor Analytics In Julia |
| Big Data and Cognitive Computing, MDPI, December 2020 |
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ABSTRACT
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| Computer games play an increasingly important role in cultural heritage preservation. They keep tradition alive in the digital domain, reflect public perception about historical events, and make history, and even legends, vivid, through means such as advanced storytelling and alternative timelines. In this context, understanding the respective underlying player base is a major success factor as different game elements elicit various emotional responses across players. To this end, player profiles are often built from a combination of low- and high-level attributes. The former pertain to ordinary activity, such as collecting points or badges, whereas the latter to the outcome of strategic decisions, such as participation to in-game events like tournaments and auctions. When available, annotations about in-game items or player activity supplement these profiles. In this article we describe how such annotations may be integrated into different player profile clustering schemes derived from a template Simon-Ando iterative process. As a concrete example, the proposed methodology has been applied to a custom benchmark dataset comprising of the player base of a cultural game. The findings are interpreted in the light of Bartle taxonomy, one of the most prominent player categorization. Moreover, the clustering quality is based on intra-cluster distance and cluster compactness. Based on these results, recommendations in an affective context for maximizing engagement are proposed for the particular game player base composition.
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| 07 December , 2020 |
| G. Drakopoulos, Y. Voutos, Ph. Mylonas, "Annotation-Assisted Clustering Of Player Profiles In Cultural Games: A Case For Tensor Analytics In Julia", Big Data and Cognitive Computing, MDPI, December 2020 |
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