Abstract

One popular Community question answering (CQA) site, Yahoo! Answers, had attracted 120 million users worldwide, and had 400 million answers to questions available. A typical characteristic of such sites is that they allow anyone to post or answer any questions on any subject. Question Answering Community has emerged as popular, and often effective, means of information seeking on the web. By posting questions, for other participants to answer, information seekers can obtain specific answers to their questions. However, CQA is not always effective: in some cases, a user may obtain a perfect answer within minutes, and in others it may require hours and sometimes days until a satisfactory answer is contributed. We investigate the problem of predicting information seeker satisfaction in yahoo collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. Our experimental results, obtained from a large scale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. We also explore automatic ranking, creating abstract from retrieved answers, and history updation, which aims to provide users with what they want or need without explicitly ask them for user satisfaction. Our system could be useful for a variety of applications, such as answer selection, user feedback analysis, and ranking.