Reflective GroupLens: Collaborative Filtering in Self-Aware Communities

推荐系统已经成为帮助用户减轻信息过载的实用工具。早期的系统服务于紧密结合的社区，但是需要用户付出大量的努力。后来的系统发展为低成本地服务于许多用户，但隐藏了推荐机制，并使用户彼此匿名。该项目一方面恢复了社区和可理解过程之间的平衡，另一方面以更少的努力为更多的人群服务。在此过程中，本文探讨了推荐系统的设计如何影响它们对用户的价值。该项目通过在现有推荐系统的背景下开发和研究新的接口，探索了三种类型的问题。首先，它检查增加用户控制或理解的界面（例如，解释和用户控制的建议）。其次，它看起来像是促进社区的接口（例如，持久身份和文本审查）。最后，它评估用户在提供界面时的行为，以选择自己的社区。这个项目的结果是更好地理解用户如何在围绕信息过滤需求建立的在线社区中进行交互。所获得的知识将对过滤系统和在线社区的设计者有用，因为他们努力通过在线协作更好地解决信息过载问题。

Recommender systems have emerged as a practical tool for helping users alleviate information overload. Early systems served close-knit communities, but required substantial effort from the users. Later systems evolved to serve many users at low effort, but hid the recommendation mechanism and left the users anonymous to each other. This project restores balance between community and understandable processes, on the one hand, and serving larger populations with less effort, on the other. In doing so, it explores how the design of recommender systems affects their value to users. The project explores three types of questions by developing and studying new interfaces in the context of an existing recommender system. First, it examines interfaces that increase user control or understanding (e.g., explanations and user-controlled recommendations). Second it looks as interfaces that foster community (e.g., persistent identities and text reviews). Finally it assesses user behavior when provided with interfaces to select their own communities. This project results in a better understanding of how users interact in on-line communities built around information filtering needs. The knowledge gained will be useful to designers of filtering systems and on-line communities as they strive to better address information overload through on-line collaboration.