Abstract
Hiring knowledgeable and cost-effective individuals, who use their knowledge and expertise to boost the organization, is extremely important for organizations as they are the most valuable assets. T-shaped experts are the best option based on agile methodology. The T-shaped professional has a deep understanding of one topic and broad knowledge of several others. Compared to other types of professionals, T-shaped professionals are better communicators and cheaper to hire. Finding T-shaped experts in a given skill area requires determining each candidate’s depth of knowledge and shape of expertise. To estimate each candidate’s depth of knowledge in a given skill area, we propose a translation-based method that utilizes two attention-based skill translation models to overcome the vocabulary mismatch between skills and user documents. We also propose two new approaches based on binary cross-entropy and focal loss to determine whether each user is T-shaped. Our experiments on three collections of the StackOverflow dataset demonstrate the efficiency of our proposed method compared to the state-of-the-art approaches.
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The dataset used in this paper is a publicly available dataset.
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Hamid Beigy reports was provided by Sharif University of Technology.
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Zohreh Fallahnejad is the coordinator and primary author of this article. She contributed significantly to the main idea and essential aspects of writing the article. Mahmood Karimian contributed to the development of certain methods presented in the article. Fatemeh Lashkari assisted in the writing process. Hamid Beigy serves as the supervisor of this article. All authors participated in the paper’s review process.
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Fallahnejad, Z., Karimian, M., Lashkari, F. et al. T-shaped expert mining: a novel approach based on skill translation and focal loss. J Intell Inf Syst (2023). https://doi.org/10.1007/s10844-023-00831-y
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DOI: https://doi.org/10.1007/s10844-023-00831-y