  Expert finding addresses the task of retrieving and ranking talented people
on the subject of user query. It is a practical issue in the Community Question
Answering networks. Recruiters looking for knowledgeable people for their job
positions are the most important clients of expert finding systems. In addition
to employee expertise, the cost of hiring new staff is another significant
concern for organizations. An efficient solution to cope with this concern is
to hire T-shaped experts that are cost-effective. In this study, we have
proposed a new deep model for T-shaped experts finding based on Convolutional
Neural Networks. The proposed model tries to match queries and users by
extracting local and position-invariant features from their corresponding
documents. In other words, it detects users' shape of expertise by learning
patterns from documents of users and queries simultaneously. The proposed model
contains two parallel CNN's that extract latent vectors of users and queries
based on their corresponding documents and join them together in the last layer
to match queries with users. Experiments on a large subset of Stack Overflow
documents indicate the effectiveness of the proposed method against baselines
in terms of NDCG, MRR, and ERR evaluation metrics.
