Abstract Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users’ expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence.
Question-Answer portals such as Naver and Yahoo! Answers are growing in popularity. However, despite the increased popularity, the quality of answers is uneven, and while some users usually provide good answers, many others often provide bad answers. Hence, estimating the authority, or the expected quality of users, is a crucial task for this emerging domain, with potential applications to answer ranking and to incentive mechanism design. We adapt a powerful link analysis methodology from the web domain as a first step towards estimating authority in Question Answer portals. Our experimental results over more than 3 million answers from Yahoo! Answers are promising, and warrant further exploration along the lines outlined in this poster. Computing the web-user's relevance to a give topic is an important task for any personalization service on the Web. Since the interest and preference of a web-user are revealed in his Web browsing history, in this paper we develop a novel approach that utilizes Web logs to compute the relevance of a web-user to a given query. In contrast to traditional methods that are purely based on textual analysis, our approach calculates the web-user's relevance through link analysis under a unified framework where the importance of web-pages and web-users mutually reinforce each other in an iterative way. The experimental results show that our approach has achieved 53 of accuracy when ranking the web-user's relevance to a search topic.