SGER: Synchronous Social-Interaction-Space Recommender Systems: Core Components Model Development and Assessment

从历史上看，推荐系统主要关注向用户推荐物品，例如书籍和电影。最近，明确的社会推荐系统出现了，它可以被归类为社会匹配（即，在线或面对面向彼此推荐个人）或社会互动空间匹配（推荐各种社交场所，如俱乐部会议，政治事件或在线聊天室）。研究人员已经开始为前者的探索奠定明确的知识基础，但后者还没有。事实上，只有少数这些系统已经部署，没有一个使用实时模型或物理或虚拟空间的混合建议。拟议的项目将通过现场和实验室的快速原型和评估来检查各种社会互动空间推荐系统设计的效用。该核心组件模型将通过探索大型复杂在线（AustNet IRC网络）和物理（NJIT校园）空间的聊天频道推荐特性来实现。最终目标是建立和维持社区，从而形成设计指南和理论框架，为系统开发人员和HCI/CSCW研究人员提供信息。更广泛的影响使用计算机技术来改善人们对社会环境的导航，使他们能够轻松地在感兴趣的活动中与他人协调，这是提高社会连通性和社会资本的一种简单方法。这是一个重要的问题，因为身处丰富联系的社会环境中的个人能够更好地处理个人挫折，如财务失败和疾病，为他人提供社会支持，并促进他们的职业发展。拥有高水平社会互动的地方社区和社区更有可能参与集体行动，并支持经济发展。因此，增加这种互动的系统具有巨大的社会价值。这个SGER将探索一种很大程度上未经测试的方式来改进社交导航，即部署同步（接近实时的）社交互动空间（聊天室、会议室、俱乐部、演讲厅等）推荐系统。该项目还将通过发布现场试验的使用数据（以适当的净化形式）和开源软件应用程序，使研究界受益。最后，为这类系统开发的新的分析匹配算法和隐私机制，以及对使用这类系统的决定因素的理解，将适用于政府和商业部门，以及学术界。

Historically, recommender systems have primarily been concerned with recommending items to users, for example, books and movies. More recently, explicit social recommender systems have emerged, which can be categorized as social-matching (i.e., recommending individuals to each other either online or face-to-face) or social-interaction-space matching (recommending various social venues such as club meetings, political events, or online chat rooms). Researchers have begun to lay clear intellectual foundations for the exploration of the former, but not the latter. In fact only a handful of these systems have been deployed and none has used real-time models or hybrid recommendations of physical or virtual spaces.The proposed project will examine the utility of various social-interaction-space recommendation system designs through rapid prototyping and evaluation in both the field and laboratory. This core components model will be achieved through an exploration of the character of chat-channel recommendations for large complex online (AustNet IRC network) and physical (NJIT campus) spaces. The ultimate goal is to build and sustain community that will result in the development of design guidelines and a theoretical framework that will inform system developers and HCI/CSCW researchers.Broader ImpactsUsing computer technology to improve people's navigation of their social environment so that they can easily coordinate with others in activities of interest is a simple way to improve social connectivity and social capital. This is an important concern, as individuals embedded in richly connected social environments are better able to handle personal setbacks such as financial failures and illness, provide social support for others, and advance their career. Local communities and neighborhoods that have high levels of social interaction are more likely to engage in collective action, and support economic development. Systems that increase such interactions are therefore of enormous social value. This SGER will enable the exploration of a largely untested way to improve social navigation, namely deploying synchronous (near real time) social-interaction-space (chat rooms, meeting rooms, clubrooms, lecture halls, etc.) recommender systems. The project will also benefit the research community through the release of usage data from the field trials (in a suitably sanitized form) and open source software applications. Finally, the novel profiling matching algorithms and privacy mechanisms developed for this class of systems, and understanding of determinants of use of such systems, will be applicable in the government and business sectors, as well as in academia.