Mining shape of expertise: A novel approach based on convolutional neural network |
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| Institution: | 1. Faculty of Computer Science and Engineering, Shahid Beheshti University, G.C., Tehran, Iran;2. Faculty of Computer Science and Engineering, University of Zanjan, Zanjan, Iran;3. VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam;1. Facultad de Ingeniería, Universidad Autónoma de Chihuahua, Circuito Universitario Campus ll Chihuahua, Chih, C.P. 31125, Mexico;2. Departamento de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. Ma. Tonantzintla, Puebla, C.P. 72840, Mexico;1. School of Remote Sensing and Information Engineering, Wuhan University, #129 Luoyu Road, Wuhan, Hubei, PR China;2. School of Software, South China Normal University, #55 West of Zhongshan Avenue, Guangzhou, Guangdong, PR China;3. School of Printing and Packaging, Wuhan University, #129 Luoyu Road, Wuhan, Hubei, PR China;4. Suzhou Institute, Wuhan University, #377 Linquan Street, Suzhou, Jiangsu, PR China;1. Center for Studies of Information Resources, Wuhan University, Bayi Rd 20299, Wuhan 430072, China;2. Department of Information Management, Nanjing University of Science and Technology, Xiaolingwei St. 200, Nanjing 210094, China |
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| Abstract: | 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. |
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