从角色自定义数据中理解玩家的身份和行为原型

Chong-U Lim, D. Harrell
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引用次数: 8

摘要

虚拟身份是人们生活中不可或缺的一部分,从网上购物账户到社交网络档案,从智能导师到视频游戏中的化身。在许多电子游戏中,玩家在虚拟环境中构建化身来代表自己,研究表明,玩家的社会文化身份会影响他们的化身构建,并可以作为推断他们在非虚拟(现实)世界中的价值观的代理。在本文中,我们提出了一种计算方法,利用在角色定制过程中收集的行为数据来建模玩家的真实身份。我们通过对玩家互动数据的原型分析来开发“行为原型”,即玩家在角色定制过程中所表现出的典型行为模式模型。我们模拟了以下模式:(1)“角色性别偏好”行为(对特定角色性别的偏好),(2)“造型师”行为(对角色不同部分的偏好,例如发型设计师、发型设计师等),以及(3)使用不同性别的角色(“性别弯曲”)或相同性别的角色(“性别同步”)的偏好。在一项有190名参与者的用户研究中,通过监督学习训练的行为原型模型在仅使用行为数据分类玩家的真实性别方面具有很高的准确性(81%)。我们表明,行为原型对于理解玩家的自定义行为、现实世界性别和虚拟角色性别是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding players' identities and behavioral archetypes from avatar customization data
Virtual identities are an integral part of peoples' lives, from online shopping accounts to social networking profiles, from intelligent tutors to videogame avatars. In many videogames, players construct avatars to represent themselves within virtual environments and research has shown that players' sociocultural identities influence their avatar construction and can be a proxy for inferring their values in the non-virtual (real) world. In this paper, we present a computational approach to modeling players' real-world identities using behavioral data collected during the avatar customization process. We used archetypal analysis on player interaction data to develop “behavioral archetypes”, which are models of prototypical behavior patterns exhibited by players during the avatar customization process. We modeled patterns of (1) “avatar gender-preferring” behaviors (preferences for a particular avatar gender), (2) “styler” behaviors (preferences for different parts of their avatars, e.g., hair-styler, head-styler, etc.,) and (3) preferences for using avatars of a different gender (“gender-bending”) or the same gender (“gender-synchronizing”) as the players'. In a user-study with 190 participants, the behavioral archetype model trained via supervised learning had high accuracy (81%) in classifying players' real-world gender using only behavioral data. We show that behavioral archetypes are effective for understanding players in terms of their customization behaviors, real-world genders, and virtual avatar genders.
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