  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.
