A Data-Driven Approach for Computationally Modeling Players' Avatar Customization Behaviors

Abstract

Avatar customization systems enable players to represent themselves virtually in many ways. Research has shown that players exhibit different preferences and motivations in how they customize their avatars. In this paper, we present a data-driven analytical approach to modeling player behavioral patterns exhibited during the avatar customization process. We used our data mining tool \textit{AIRvatar} to analyze telemetry data obtained from 190 players using an avatar creator of our own design. Using non-negative matrix factorization (NMF) and N-gram models, we demonstrate how our approach computationally models behavioral patterns exhibited by players such as "regular shopping," "engaged shopping," or "bored browsing". Our models obtained significant effect sizes (0.12 <= R^2 <= 0.54) when validated with multiple linear regressions for players' time spent engaging in activities within the avatar creator. The NMF model had comparably high performance and ease of interpretation compared to control models.