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.

专家发现解决了根据用户查询主题对人才进行检索和排名的任务。这是社区问答网络中的一个实际问题。为专业职位寻找知识渊博的招聘人员是专家查找系统的最重要客户。除了员工的专业知识外，雇用新员工的成本也是组织的另一大关注点。解决此问题的有效方法是聘请具有成本效益的T型专家。在这项研究中，我们为基于卷积神经网络的T型专家发现提出了一种新的深度模型。所提出的模型试图通过从相应文档中提取局部和位置不变特征来匹配查询和用户。换一种说法，它通过同时从用户文档和查询中学习模式来检测用户的专业技能状况。所提出的模型包含两个并行的CNN，可根据用户和文档的相应文档提取用户和查询的潜在向量，并将它们在最后一层连接在一起，以将查询与用户匹配。对大量Stack Overflow文档进行的实验表明，该方法相对于NDCG，MRR和ERR评估指标相对于基线的有效性。