I-Corps: A Social Platform that Models User Identity Via Interactive Stories

这个I-Corps项目的更广泛的影响/商业潜力源于它在系统中为不同用户定制互动媒体体验的效用，包括工作场所学习软件、流媒体、教育游戏和推荐应用程序。这是通过该项目在软件中实现更细微的用户表示（在线帐户、用户配置文件、虚拟形象等）的新方法实现的。目前，针对特定用户表示的定制系统响应的生成成本很高，而且不够表达：它们通常是硬编码的，实现起来需要大量的劳动，并且不能支持不同用户的特定需求。在大多数这样的系统中，用户在类别中的成员资格（例如，人口统计组或专家与新手）是以自顶向下的方式确定的，系统不可能响应仅部分适合类别、驻留多个类别或随时间变化的用户表示。这些缺陷阻碍了系统在日常生活中实现社会类别成员的细微差别。最后，一个关键的潜在应用是建立支持亲社会技能和知识发展的互动媒体。这个I-Corps项目将推进与商业和社会影响数字媒体系统中用户身份建模相关的知识。该项目的核心技术是一个计算引擎，它通过数学计算跨多个类别的用户的成员程度来建模身份（使用来自配置文件、头像、角色和帐户的数据）。为了计算类别隶属度的类别模型，引擎计算一个接近值，该接近值对应于参与者偏离类别原型成员的程度，该类别原型成员是通过一组特征定义的。在基于用户行为的交互式叙述中，成员的程度是波动的。这些场景的建模借鉴了社会语言学、认知科学和分类社会学的具体概念。它们包括：类别梯度、类别动态、多重成员关系、类别间关系和原型。除了大量的出版物外，相关的先前工作还通过受邀在国际主要大学、基金会和学术场所进行主题演讲和演讲来传达。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project results from its utility for customizing interactive media experiences for diverse users in systems including workplace learning software, streaming media, educational games, and recommendation applications. This is enabled by the project's novel approach to implementing more nuanced user representations in software (online accounts, user profiles, avatars, etc.). Currently, customized systems' responses for specific user representations are expensive to produce and not expressive enough: they often are hardcoded, labor intensive to implement, and fail to support the particular needs of diverse users. In most such systems users' membership in categories (e.g., demographic groups or experts vs. novices) is determined in a top-down fashion, with no possibility for systems to respond to user representations that only partially fit in categories, inhabit multiple categories, or change over time. These deficiencies prevent systems from achieving the nuance of social category membership in everyday life. Finally, a key potential application is building interactive media supporting prosocial skill and knowledge development.This I-Corps project will advance knowledge related to modeling user identities in commercial and socially impactful digital media systems. Key to the intellectual merit of the project, the core technology is a computational engine that models identities (using data from profiles, avatars, characters, and accounts) by mathematically computing users' degrees of membership across multiple categories over time. To computationally model category degrees of category membership, the engine computes a closeness value corresponding to the degree to which an actor deviates from a prototypical member of a category, who is defined via a set of features. The degree of membership fluctuates throughout an interactive narrative based on user behaviors. Modeling these scenarios draws upon specific concepts from sociolinguistics, cognitive science, and sociology of classification. They include: category gradience, category dynamics, multiple memberships, inter-category relationships, and prototypes. Along with numerous publications, relevant prior work has been conveyed through invited keynotes and talks at major universities, foundations, and academic venues internationally.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.