QFami: An Integrated Environment for Recommending Answerers on Campus

With the development of the Internet, people increasingly use search engines to obtain the answers for their questions and quench their thirst for knowledge. Although search engines often provide some information quickly, they can fail to provide what people really need. Despite the ubiquitous availability of search engines, people ask questions to other people to seek valuable local information and enrich social experiences. Existing social applications such as Stack Overflow [1] and other Community Question Answering systems allow people to exchange knowledge efficiently. However, they mainly consider the expertise to recommend answerers, which would not be sufficient for supporting Q&A activities in real-world environments. In this work, we propose QFami, a novel integrated Q&A environment for physically-based Q&A scenarios on campus, QFami incorporates the interest expertise, proximity, locations, and other contextual factors that can be inferred by using various sensors on mobile phones.