SBIR Phase I: Blue Shift Communities - Improving Online Technical Support

这个小企业创新研究（SBIR）第一阶段项目确定了一个商业机会，以改善在线技术支持软件。 该公司提出了两项互补的创新，以促进可扩展的高质量技术支持。第一，是一个新的系统，用于建立技术支持社区，通过鼓励综合和减少重复工作，产生具有持久价值的信息产品。该系统结合了问题和答案（Q A）的网站与维基启发的内容合成和细化机制的功能。第二，开发基于机器学习的方法来检测低质量的用户贡献，例如火焰和愤怒的咆哮。该公司将试验文本处理的上下文无关模型和上下文丰富的用户模型，以确定最强大的功能集，用于算法预测用户贡献的质量。互补的创新有一个主要目标：改善在线技术支持社区的用户体验。通过提供更容易使用的工具，该公司将使提出问题和搜索技术问题的答案变得更容易。通过使低质量的帖子更容易被审核，该公司将减轻管理员的负担，并提高普通用户的信噪比。通过建立鼓励综合和阻止重复的工具，该公司有潜力创建高质量的信息库，使广泛的参与者受益。

This Small Business Innovation Research (SBIR) Phase I project identifies a commercial opportunity to improve online technical support software. The company propose two complementary innovations that facilitate scalable, high-quality technical support. First, is a new system for building technical support communities that generate informational artifacts of lasting value by encouraging synthesis and reducing duplication of effort. The system combines the features from question and answer (Q&A) sites with wiki-inspired mechanisms for content synthesis and refinement. Second, is the development of machine learning-based methods for detecting low-quality user contributions, such as flames and angry rants. The company will experiment with both context-free models of text processing and context-rich user models to determine the most robust feature set for algorithmically-predicting the quality of a user contribution.The complementary innovations share one primary goal: to improve the user experience in online technical support communities. By providing tools that are easier to use, the company will make it easier to ask questions and search for answers to technical problems. By making it easier to moderate low quality posts, the company will ease the burden on administrators, and increase the signal-to-noise ratio for regular users. By building tools that encourage synthesis and discourage duplication, the company has the potential to create high quality repositories of information that benefit a wide array of constituents.