Mining shape of expertise: A novel approach based on convolutional neural network

• Published in: Information processing & management Vol. 57; no. 4; p. 102239
• Main Authors: Dehghan, Mahdi, Rahmani, Hossein Ali, Abin, Ahmad Ali, Vu, Viet-Vu
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2020; Elsevier Science Ltd
• Subjects: Artificial neural networks; Community question answering; Data mining; Datasets; Deep neural network; Expert finding; Feature extraction; Information retrieval; Neural networks; Queries; T shape; T-Shaped mining; Deep neural network; Community question answering; Expert finding; T-Shaped mining
• DOI: 10.1016/j.ipm.2020.102239
• ISSN: 0306-4573

•Detecting shape of expertise is a practical and industry-motivated problem.•A CNN-based model was proposed in this study to detect users’ shape of expertise.•The proposed method is based on matching both latent vectors of users and queries. 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.