Online knowledge-sharing communities typically acknowledge their top contributors by implementing recognition systems. Extant recognition systems suffer from several limitations such as treatment of the entire community as homogeneous and inflexibility of customization. We propose a framework based on socio-technical design principles for designing a multi-criterion- and multi-segment-based recognition system that exploits multiple user characteristics for differential recognition according to community goals. We apply this framework on data gathered from Yelp.com and show how it can be used to recognize top members of different identified behavioral segments (amateurs, adepts, and enthusiasts) based on their performance on various relevant factors.

在线知识共享社区通常通过实施认可系统来表彰其杰出贡献者。现有的识别系统存在一些局限性，例如将整个社区视为同质和定制的灵活性。我们提出了一个基于社会技术设计原则的框架，用于设计基于多标准和多细分的识别系统，该系统利用多个用户特征根据社区目标进行差异识别。我们将此框架应用于从Yelp.com收集的数据，并展示如何根据其在各种相关因素上的表现来识别不同的已识别行为群体（业余者，熟练者和爱好者）的最高成员。