  Knowledge acquisition and exchange are generally crucial yet costly for both
businesses and individuals, especially when the knowledge concerns various
areas. Question Answering Communities offer an opportunity for sharing
knowledge at a low cost, where communities users, many of whom are domain
experts, can potentially provide high-quality solutions to a given problem. In
this paper, we propose a framework for finding experts across multiple
collaborative networks. We employ the recent techniques of tree-guided learning
(via tensor decomposition), and matrix factorization to explore user expertise
from past voted posts. Tensor decomposition enables to leverage the latent
expertise of users, and the posts and related tags help identify the related
areas. The final result is an expertise score for every user on every knowledge
area. We experiment on Stack Exchange Networks, a set of question answering
websites on different topics with a huge group of users and posts. Experiments
show our proposed approach produces steady and premium outputs.
